SETTLING-TIME IMPROVEMENT IN GLOBAL CONVERGENCE LAGRANGIAN NETWORKS

Leonardo Acho

In this brief, a modification of Lagrangian networks given in (X. Youshen, 2003) is presented. This modification improves the settling time of the convergence of Lagrangian networks to a stationary point; which is the optimal solution to the nonlinear convex programming problem with linear equality constraints. This is important because, in many real-time applications where Lagrangian networks are used to find an optimal solution, such as in signal and image processing, this settling time is interpreted as the processing time. Simulation results applied to a quadratic optimization problem show that settling time is improved from about to 2000 to 20 seconds. Lyapunov theory was used to obtain our main result.

ADAPTIVE FUZZY SLIDING MODE CONTROLLER FOR THE SNORKEL UNDERWATER VEHICLE

Eduardo Sebastián and Miguel Ángel Sotelo Vázquez

This paper describes a control system for the kinematic variables of an underwater vehicle. Control of underwater vehicles is not simple, mainly due to the nonlinear, coupled and unknown character of system equations and dynamics. The proposed methodology makes use of a pioneer algorithm implemented for the first time in an underwater vehicle, and it is based on the fusion of a sliding mode controller and an adaptive fuzzy system, including advantages of both systems and relaxing the required knowledge of vehicle model.

ROBUST FUZZY CONTROLLER DESIGN FOR UNCERTAIN DESCRIPTOR MARKOVIAN JUMP SYSTEMS

Wudhichai Assawinchaichote and Sing Kiong Nguang

This paper examines the problem of designing a robust H_infty state-feedback controller for a class of uncertain nonlinear descriptor Markovian jump systems described by a Takagi-Sugeno (TS) fuzzy model with Markovian jumps. Based on a linear matrix inequality (LMI) approach, LMI-based sufficient conditions for the uncertain nonlinear descriptor Markovian jump systems to have an H_infty performance are derived. The proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard, but also to nonstandard nonlinear descriptor systems. A numerical example is provided to illustrate the design developed in this paper.

LOOKING FOR MASCONTROL: A MULTIAGENT SYSTEM FOR IDENTIFICATION AND CONTROL

E. J. González, Alberto Hamilton, L. Moreno, R. L. Marichal, J.A. Méndez and Vanessa Muñoz

In this paper, MASCONTROL, a multiagent system (MAS) for system identification and process control, is presented. This MAS implements a self-tuning regulator (STR) scheme. In this work, an Ontology Agent (OA) is included, using DAML+OIL as ontology language. From their experience, the authors consider this architecture highly useful for identification and control processes.

DEFECTIVE METAL END DETECTION WITH A FUZZY SYSTEM

Perfecto Mariño Espiñeira, Vicente Pastoriza Santos, Miguel Santamaría Sánchez and Emilio Martínez Expósito

The authors have been involved in developing an automated inspection system, based on machine vision, to improve the repair coating quality control (RCQ control) in can ends of metal containers for fish food. The RCQ of each end is assesed estimating its average repair coating quality (ARCQ). In this work we present a fuzzy model building to make the acceptance/rejection decision for each can end from the information obtained by the vision system. In addition it is interesting to note that such model could be interpreted and supplemented by process operators. In order to achieve such aims, we use a fuzzy model due to its ability to favour the interpretability for many applications. Firstly, the easy open can end manufacturing process, and the current, conventional method for quality control of easy open can end repair coating, are described. Then, we show the machine vision system operations. After that, the fuzzy modeling, results obtained and their discussion are presented. Finally, concluding remarks are stated.

A HIERARCHICAL FUZZY-NEURAL MULTI-MODEL: An application for a mechanical system with friccion identification and control

Ieroham Baruch, Jose Luis Olivares and Federico Thomas

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation learning method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposes the application of two control schemes, which are: a trajectory tracking control by an inverse FNHMM and a direct adaptive control, using the states issued by the identification FNHMM. The proposed control methods are applied for a mechanical plant with friction system control, where the obtained comparative results show that the control using FNHMM outperforms the fuzzy and the neural control itself.

A FAST TABU SEARCH ALGORITHM FOR FLOW SHOP PROBLEM WITH BLOCKING

Jozef Grabowski and Jaroslaw Pempera

This paper develops a fast tabu search algorithm to minimize makespan in a flow shop problem with blocking. We present a fast heuristic algorithm based on tabu search approach. In the algorithm the multimoves are used that consist in performing several moves simultaneously in a single iteration of algorithm and guide the search process to more promising areas of the solutions space, where good solutions can be found. It allow us to accelerate the convergence of the algorithm. Besides, in the algorithm a dynamic tabu list is used that assists additionally to avoid trapped at a local optimum. The proposed algorithm is empirically evaluated and found to be relatively more effective in finding better solutions in a much shorter time.

FUZZY DIAGNOSIS MODULE BASED ON INTERVAL FUZZY LOGIC: OIL ANALYSIS APPLICATION

Antonio Sala, Bernardo Tormos, Vicente Macián and Emilio Royo

This paper presents the basic characteristics of a prototype fuzzy expert system for condition monitoring applications, in particular, oil analysis in Diesel engines. The system allows for reasoning under absent or imprecise measurements, providing with an interval-valued diagnostic of the suspected severity of a particular fault. A set of so-called metarules complements the basic fault dictionary for fine tuning, allowing extra functionality.

DISCRETE–TIME FREE AND FIXED END-POINT OPTIMAL CONTROL PROBLEM

Corneliu Botan and Florin Ostafi

A comparison between the fixed and free end-point discrete time linear quadratic optimal problem is performed. Symmetrical algorithms for both problems are proposed. These algorithms can be easier implemented by comparison with classical procedures. Simulation results are presented.

OPTIMIZED FUZZY SCHEDULING OF MANUFACTURING SYSTEMS

Nikolaos Tsourveloudis, Lefteris Doitsidis and Stratos Ioannidis

In this paper an Evolutionary Algorithm (EA) strategy for the optimization of generic Work-In-Process (WIP) scheduling fuzzy controllers is presented. The EA strategy is used to tune a set of fuzzy control modules that are used for distributed and supervisory WIP scheduling. The distributed controllers objective is to control the rate in each production stage in a way that satisfies the demand for final products while reducing WIP within the production system. The EA identifies those set of parameters for which the fuzzy controller performs optimal with respect to WIP and backlog minimization. The proposed EA strategy is compared with known heuristically tuned distributed and supervised fuzzy control approaches. Extensive simulation results show that the EA strategy significantly improves system’s performance.

MODEL PREDICTIVE CONTROL FOR DISTRIBUTED PARAMETER SYSTEMS USING RBF NEURAL NETWORKS

Eleni Aggelogiannaki and Haralambos Sarimveis

A new approach for the identification and control of distributed parameter systems is presented in this paper. A radial basis neural network is used to model the distribution of the system output variables over space and time. The neural network model is then used for synthesizing a non linear model predictive control configuration. The resulting framework is particular useful for control problems that pose constraints on the controlled variables over space. The proposed scheme is demonstrated through a tubular reactor, where the concentration and the temperature distributions are controlled using the wall temperature as the manipulated variable. The results illustrate the efficiency of the proposed methodology.

EMBEDDED ROBOTIC CONTROL TECHNOLOGIES AND ITS APPLICATIONS IN AUTOMATED PROGRAMMERS

Ganwen Zeng and Kelly Hirsch

The paper presents a synthesis of the problematic and actual solutions to the implementation of robotic programmer control functionality using DSP controllers. Considerable technology shift occurred during the recently decade in device programming industry. The advent of high performance DSP motion controllers opens new possibilities for the development of high performance distributed intelligence device-programming automation systems. The idea of implementing a unique, flexible robotic motion control structure can significantly improve controllability of the robotic programming systems. High-level motion command languages are used to setup and to control the robotic motors. A Fuzzy control algorithm has been introduced to guarantee the motion control performance in an automated programmer.

MODELLING HYBRID CONTROL SYSTEMS WITH BEHAVIOUR NETWORKS

Pierangelo Dell'Acqua, Anna Lombardi and Luís Moniz Pereira

We present an approach to model adaptive, dynamic hybrid control systems based on behaviour networks. We extend and modify the approach to behaviour networks with integrity constraints, non-ground rules, internal actions, and modules to make it self-adaptive and dynamic. The proposed approach is general, reconfigurable, robust, and suitable for environments that are dynamic and too complex to be entirely predictable, the controlling system having limited computational and time resources.

LQG CONTROL UNDER AMPLITUDE AND VARIANCE CONSTRAINTS

A. Królikowski, D. Horla and T. Kubiak

In this paper, the amplitude and variance-constrained LQG control is considered for a plant given by discrete-time ARMAX model. The minimization of constrained quadratic cost is approached by Kalman filter, approximation of the probability density function (pdf) of the state by the Gaussian one and by by tuning of the Lagrange multiplier. The obtained optimization algorithm is simulated for second-order stable plant model and different constraints.

ONTOLOGY FOR INTEGRATING HETEROGENEOUS TOOLS FOR SUPERVISION, FAULT DETECTION AND DIAGNOSIS

Beatriz López, Joaquim Meléndez and Silvia Suárez

The Distributed Supervision Systems that have been used extensively for the last fifteen years in the process industry are now evolving towards higher level solutions based on better connections between applications and processes that assure that data flows from the process to manage boards. Knowledge sharing seems to be a key issue in integrating these heterogeneous systems. In this paper we present an ontology as a first step to achieving semantic interoperability. The ontology has been conceived within the context of a complex integration problem, in which heterogeneous toolboxes cooperate to deal with several supervision, fault detection and diagnostic tasks for chemical processes. Regarding the current trends in ontology research, our proposal is consistent with top-level ontologies, as these kinds of ontologies seem to overcome the ontology integration problem. We describe a preliminary version of the ontology. The conceptualisation of control variables, system behaviour, supervision tasks, models and system properties is given. All attributes and relationships between each concept has been deployed. The ontology has been developed using Protete2000.

USE OF THE COG REPRESENTATION TO CONTROL A ROBOT WITH ACCELERATION FEEDBACK

Frédéric Colas, Eric Dumetz, Pierre-Jean Barre and Jean-Yves Dieulot

A controller using acceleration feedback has been applied to a flexible robot for which the position and velocity of the load are not measured. It is shown by using the Causal Ordering Graph (COG), that the motor can be controlled by using acceleration feedback and that it allows an exact tracking of the motor position, irrespective of the non-linear flexibilities of the axis and of the measurement disturbances. This easy-to-tune algorithm, which main control parameters are the modal masses of the motor and load part and only consists of a positive acceleration feedback plus a PD controller, has been validated on an industrial 3-axis robot.

FUZZY ADAPTIVE CONTROLLER FOR A SYNCHRONOUS MACHINE

Gregorio Drayer and Miguel Strefezza

This paper presents the comparison of applying an adaptive fuzzy controller with and without a variable structure controller (VSC) for a synchronous machine. A simplified linear model of the synchronous machine connected to an infinite bus with constant impedance is used. The multivariable system was previously decoupled to make easier the application of the control schemes. To control the system, an adaptive Fuzzy PD controller is proposed and it acts both on the load variable and on the voltage variable. Then, a Fuzzy Adaptive System is designed to act over the Fuzzy controller. After this, the VSC theory is applied to the Adaptive Controller to compare both strategies. Simulation results using these two control schemes are presented. With these proposed actions, the results show a better transitory response of the system when compared with the system response using classical control.

METHOD TO IMPROVE THE TRANSPARENCY OF NEUROFUZZY SYSTEMS

J. A. Domínguez-López

Neurofuzzy systems have been widely applied to a diverse range of applications because their robust operation and network transparency. A neurofuzzy system is specified by a set of rules with confidences. The use of rule confidences rather than a weight vector allows the model to be represented as a set of transparent fuzzy rules. Nevertheless, as knowledge base systems, neurofuzzy systems suffer from the curse of dimensionality i.e., exponential increase in the demand of resources and in the number of rules. Accordingly, the interpretability of the final model can be lost. Consequently, it is desired to have a simple rule-base to ensure transparency and implementation efficiency. After training, a rule can have several non-zero confidences. The more number of non-zero confidences, the less transparent the final model becomes. Therefore, it is elemental to reduce the number of non-zero confidences. To achieve this, the proposed algorithm search for (a maximum of) twon on-zero confidences which give the same result. Thus, the system can keep its complexity with a better transparency. The proposed methodology is tested in a practical control problem to illustrate its effectiveness.

GENETIC AND ELLIPSOID ALGORITHMS FOR NONLINEAR PREDICTIVE CONTROL

Kaouther Laabidi, Faouzi Bouani and Mekki Ksouri

This paper deals with the constrained predictive control of nonlinear systems. The Artificial Neural Networks (ANN) are used as a process model. The control law is derived by minimizing a non convex criterion. The optimization problem is solved using Ellipsoid and genetic algorithms. The structure and operators of the combining two algorithms have been specifically developed for control design problem. Simulation results are presented to illustrate the performance of the proposed predictive controller.

BIOPRODUCTS DRYING OPTIMAL CONTROL IN OSCILLATING REGIMES

Renat Sadykov, Dmitry Antropov and Rauf Kafiatullin

On the basis of the developed approaches and mathematical model (MM) of the bioactive products drying block is carried out the optimization problem of the equipment choice and its operation modes in view of deleted binary mixture an ethanol - water composition changes. The analysis of the problem with engaging of the Pontryagin’s maximum principle has revealed optimal control structure. There is developed the automated control system of drying installation with firmware, based on modern microprocessor technique. The guidelines on an drying processes intensification, worked out on the basis of the internal and external interconnected heatmasstransfer research, and the process optimal control considerably raise productivity of drying aggregates, reduce fuel and power expenditures.

SELF-LEARNING DISTURBANCE COMPENSATION FOR ACTIVE SUSPENSION SYSTEMS

Eckehard Münch, Henner Vöcking and Thorsten Hestermeyer

Ride comfort and safety of vehicles can be increased by active suspension systems. A problem is the detection of disturbances which can generally not be measured until they impact the chassis. Provided guidance and disturbance are known in advance, a controller can use this information to achieve considerably improved behavior. This paper presents an approach in which railway vehicles coupled in a network, in repeated runs over the same track section, learn a disturbance compensation that can almost entirely compensate for stationary disturbances, i.e., disturbances that occur at the same spot in equal measure. Here information on the respective track section is sampled, stored locally at the track, and retrieved by the succeeding vehicle which will use them for an improved compensation for the occurring disturbances and again store information there. This iterative procedure results in an optimal compensation. The algorithm is described and criteria for its design are derived from digital control theory. The procedure was implemented on a testbed for a semi-vehicle with three degrees of freedom. The results of the measurements are displayed and evaluated in this paper.

STABLE REPETITIVE CONTROL BY FREQUENCY ALIASING

James D. Ratcliffe, Paul L. Lewin, Eric Rogers, Jari J. Hätönen, Thomas J. Harte and David H. Owens

A filtering technique based on frequency aliasing which was initially developed for Iterative Learning Control is modified so that it can be implemented in real-time and is suitable for Repetitive Control. The aliasing technique is experimentally verified on a gantry robot facility, which manipulates payloads from a dispenser onto a constant velocity conveyor. A parallel arrangement consisting of a three-term feedback controller and a simple structure repetitive controller is used to control the robot. Without the aliasing technique, the combined control system becomes unstable very rapidly. In contrast, when the aliasing technique is applied, 1000 repetitions are successfully completed and no indications of impending instability can be seen.

MICROSILICON LUMINOUS FLUX SWITCH CONTROLLED BY MEANS OF MAGNETIC FIELD

J. Gołębiowski, T. Prohuń

The construction of a silicon beam which is used as a optical switch was presented. The investigated beam consists of three layers: on the silicon base the iron layer is put and it is followed by the aluminium layer. The change of the external magnetic field intensity causes the beam end displacement as well as the change of the luminous flux reflection angle. The influence of the magnetic transducer parameters as well as the field intensity on the luminous flux reflection angle are analysed. The optical system which is steered by the magnetic field was described.

GA BASED DATA FUSION APPROACH IN AN INTELLIGENT INTEGRATED GPS/INS SYSTEM

Ali Asadian, Behzad Moshiri, Ali Khaki Sedigh, and Caro Lucas

A new concept regarding to the GPS/INS integration, based on artificial intelligence here is presented. Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, an INS/GPS integration method using a hybrid adaptive network based fuzzy inference system (ANFIS) has been proposed in leterature. The advantage of the ANFIS over other classical filtering algorithms is its ability to deal with noise in the input data in dynamic environments. During the availability of GPS signal, the ANFIS is trained to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. As ANFIS will be employed in real time applications, the change in the system parameters (e.g., the number of membership functions, the step size, and step increase and decrease rates) to achieve the minimum training error during each time period is automated. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS Integration in comparison with conventional ANFIS specially in the cases when facing satellites’ outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

A SCHEDULING TECHNIQUE OF PLANS WITH PROBABILITY AND TEMPORAL CONSTRAINTS

Bassam Baki and Maroua Bouzid

The paper describes a constraint programming approach for generating partially ordered plans with durative actions and probabilities. We propose a planner that generates a plan represented in the form of a set of tasks. Each task has a set of temporal constraints, a set of probabilities and a set of constant costs. All tasks form an acyclic AND/OR Graph in which our planner will find a plan formed by a set of tasks chosen to be executed in order to achieve a goal under specified constraints. This paper describes one approach to deal with a problem that has paid a little attention of planing community. This problem is to combine temporal and probabilistic planning.

INTEGRATED FEED-FORWARD ARTIFICIAL NEURAL NETWORKS SYSTEM FOR MACHINES TOOLS SELECTION

Romdhane Ben Khalifa, Noureddine Ben Yahia and Ali Zghal

We propose in this paper an integration module of the automatic choice of the machine tools in the environment of the systems CAD/CAM, which consisted in the two neuronal systems NN1 and NN2; NN1 allows the automatic choice of machining machines. NN2 makes it possible to choose cutting tools for machining features. In this work, we worked out two complementary parts for the integration of the automatic choice of machine tools. Firstly we developed a neuronal system for selection of machine tools classes. Secondly, one created an interface of integration of neuronal system which exploits the machining features geometrical data to be carried out by the programming Visual Basic

A HYBRID DECISION SUPPORT SYSTEM - The joint use of Simulation, Coloured Petri Nets and Expert System

Fabiano A. Hennemann, Ricardo J. Rabelo, José E. R. Cury, José V. Canto dos Santos and Arthur T. Gómez

This works aim to propose a Hybrid Decision Support System (HDSS), based in Simulation and Coloured Petri Nets as modelling techniques of manufacture processes, and an Expert System to assist in its use. The HDSS provides a friendly interface for the user that, after selecting input parameters, gets as answer a series of data about the manufacturing process that will assist in the evaluation of its performance. To validate the proposal, some particular scenes have been tested, with objective to elaborate a set of proposals for improving the performance of productive systems, evaluating the impacts from the change on model parameters and providing a better understanding about the systems considered. The HDSS makes it possible for managers, without knowledge of modelling techniques, to manipulate data and to interact with the developed model. The developed prototype was made generic for applying on general manufacturing processes, so that it is possible to use it for any industrial plant, provided that the input parameters of the model are adequately fitted, using the data input interface of the system.

APPLICATION OF DE STRATEGY AND NEURAL NETWORK - In position control of a flexible servohydraulic system

Hassan Yousefi and Heikki Handroos

One of the most promising novel evolutionary algorithms is the Differential Evolution (DE) algorithm for solving global optimization problems with continuous parameters. In this article the Differential Evolution algorithm is proposed for handling nonlinear constraint functions to find the best initial weights of neural networks. The highly non-linear behaviour of servo-hydraulic systems makes them idea subjects for applying different types of sophisticated controllers. The aim of this paper is position control of a flexible servo-hydraulic system by using back propagation algorithm. The poor performance of initial training of back propagation motivated to apply the DE algorithm to find the initial weights with global minima. This study is concerned with a second order model reference adaptive position control of a servo-hydraulic system using two artificial neural networks. One neural network as an acceleration feedback and another one as a gain scheduling of a proportional controller are proposed. The results suggest that if the numbers of hidden layers and neurons as well as the initial weights of neural networks are chosen well, they improve all performance evaluation criteria in hydraulic systems.

AN EXPLORATION MEASURE OF THE DIVERSITY VARIATION IN GENETIC ALGORITHMS

George Papakostas and Yiannis Boutalis

In this paper, a novel measure of the population diversity of a Genetic Algorithm (GA) is presented.Chromosomes diversity plays a major role for the successfully operation of a GA, since it describes the number of the different candidate solutions that the algorithm evaluates, in order to find the optimal one, in respect to a performance index, called objective function. In a well defined algorithm, the diversity of the current population should be measurable, in order to estimate the performance of the algorithm. The resulted observation, that is, the measuring of the diversity, can then be used to real-time adjust the factors that determine the chromosomes variety (Pc, Pm), during the execution of the GA. It is shown, that a simple chromosomes clustering into the search space, by using the well known k-means algorithm, can give a useful picture of the population’s distribution. Thus, by translating the problem of finding the best solution to a GA-based problem into an iterative clustering process, and by using the scatter matrices (Sw, Sb), which describe completely the candidate’s solutions topology, one could define a novel formula that gives the population diversity of the algorithm.

A NOVEL REPRESENTATION AND ALGORITHMS FOR (QUASI) STABLE MARRIAGES

B. Y. Zavidovique, N. Suvonvorn and Guna S. Seetharaman

In this paper, we propose "stable marriages" algorithms based on a novel representation called "marriage table". After explaining how properties as global satisfaction, sex equality and stability show in the representation, we define 3 algorithms corresponding to 3 different scans of the "marriage table" to meet progressively all constraints. The performance is evaluated in front of the population size for 200 instances in each case. That supports qualitative statistic analysis. Two matching examples in image processing are displayed for illustration.

KNOWLEDGE REPRESENTATION APPROACH TO CLOSED LOOP CONTROL SYSTEM - A TANK SYSTEM CASE-STUDY

Luís Rato, Irene Pimenta Rodrigues and Rui Gomes

Control engineering problems are dealt within a plethora of methods and approaches depending on the a priori knowledge, the description of the process to control, and the main control goal. Classical control theory is mainly based on properties of numerical models. This paper presents an approach that applies to a class of processes described by numerical and logical relations using inference and a knowledge base system. To attain this goal an ontology for control systems is constructed. The work presented in this paper is based in a three tank system benchmark.

D3G2A: A DYNAMIC DISTRIBUTED DOUBLE GUIDED GENETIC ALGORITHM FOR THE CASE OF THE PROCESSORS CONFIGURATION PROBLEM

BOUAMAMA Sadok and Khaled GHEDIRA

Within the framework of Constraint satisfaction and optimization problem (CSOP), we introduce a new optimization distributed method based on Genetic Algorithms (GA). This method consists of agents dynamically created and cooperating in order to solve the problem. Each agent performs its own GA on its own sub-population. This GA is sometimes random and sometimes guided by both the template concept and by the Min-conflict-heuristic. In addition with guidance, our approach is based on NEO-DARWINISM theory and on the nature laws. In fact, by reference to their specificity the new algorithm will let the agents able to count their own GA parameters. In order to show D3G2A advantages, experimental comparison with GGA is provided by their application on the Large processors configuration Problem.

OPTIMIZATION IN RAILWAY SCHEDULING

M. A. Salido, M. Abril, F. Barber, L. Ingolotti, A. Lova and P. Tormos

Train scheduling has been a significant issue in the railway industry. Over the last few years, numerous approaches and tools have been developed to aid in the management of railway infrastructure. In this paper, we present two filtering techniques for a constraint-based train scheduling tool, which is a project in collaboration with the National Network of Spanish Railways (RENFE), Spain. We formulate train scheduling as constraint optimization problems. Two filtering techniques are developed to speed up and direct the search towards suboptimal solutions in periodic train scheduling problems. The feasibility of our problem-oriented techniques are confirmed with experimentation using real-life data. The results show that these techniques enables MIP solvers such as LINGO and ILOG Concert Technology (CPLEX) to terminate earlier with good solutions.

DERIVING BEHAVIOR FROM GOAL STRUCTURE FOR THE INTELLIGENT CONTROL OF PHYSICAL SYSTEMS

Richard Dapoigny, Patrick Barlatier, Eric Benoit and Laurent Foulloy

Given a physical system described by a structural decomposition together with additional constraints, a major task in Artificial Intelligence concerns the automatic identification of the system behavior. We will show in the present paper how concepts and techniques from different AI disciplines help solve this task in the case of the intelligent control of engineering systems. Following generative approaches grounded in Qualitative Physics, we derive behavioral specifications from structural and equational information input by the user in the context of the intelligent control of physical systems. The behavioral specifications stem from a teleological representation based on goal structures which are composed of three primitive concepts, i.e. physical entities, physical roles and actions. An ontological representation of goals extracted from user inputs facilitates both local and distributed reasoning. The causal reasoning process generates inferences of possible behaviors from the ontological representation of intended goals. This process relies on an Event Calculus approach. An application example focussing on the control of an irrigation channel illustrates the behavioral identification process.

FEASIBLE CONTROL OF COMPLEX SYSTEMS USING AUTOMATIC LEARNING

Alejandro Agostini and Enric Celaya

Robotics applications often involve dealing with complex dynamic systems. In these cases coping with control requirements with conventional techniques is hard to achieve and a big effort has to be done in the design and tuning of the control system. An alternative to conventional control techniques is the use of automatic learning systems that could learn control policies automatically, by means of the experience. But the amount of experience required in complex problems is intractable unless some generalization is performed. Many learning techniques have been proposed to deal with this challenge but the applicability of them in a complex control task is still difficult because of their bad learning convergence or insufficient generalization. In this work a new learning technique, that exploits a kind of generalization called categorization, is used in a complex control task. The results obtained show that it is possible to learn, in short time and with good convergence, a control policy that outperforms a classical PID control tuned for the specific task of controlling a manipulator with high inertia and variable load.

MULTIOBJECTIVE OPTIMAL DESIGN OF STRUCTURE AND CONTROL OF A CONTINUOUSLY VARIABLE TRANSMISSION

Jaime Alvarez-Gallegos, Carlos A. Cruz-Villar and Edgar A. Portilla-Flores

An approach to solve the mechatronic design problem is to formulate the problem as a multiobjective dynamic optimization problem (MDOP), where kinematic and dynamic models of the mechanical structure and the dynamic model of the controller are considered besides a set of constraints and a performance criteria. This design methodology can provide a set of optimal mechanical and controller parameters so that the desired dynamic behavior and the performance criteria are satisfied. In this paper a MDOP is proposed and applied to a continuously variable transmission (CVT). Performance criteria are the mechanical efficiency and the minimal controller energy. The goal attainment method and a sequential approach are used to solve the MDOP.

CONTRIBUTORS TO A SIGNAL FROM AN ARTIFICIAL CONTRAST

Jing Hu, George Runger and Eugene Tuv

Data from a process or system is often monitored in order to detect unusual events and this task is required in many disciplines. A decision rule can be learned to detect anomalies from the normal operating environment when neither the normal operations nor the anomalies to be detected are pre-specified. This is accomplished through artificial data that transforms the problem to one of supervised learning. However, when a large collection of variables are monitored, not all react to the anomaly detected by the decision rule. It is important to interrogate a signal to determine the variables that are most relevant to or most contribute to the signal in order to improve and facilitate the actions to signal. Metrics are presented that can be used determine contributors to a signal developed through an artificial contrast that are conceptually simple. The metrics are shown to be related to traditional tools for normally distributed data and their efficacy is shown on simulated and actual data.

JAVA BASED TOOLBOX FOR LINEAR REPETITIVE PROCESSES

J. Gramacki, A. Gramacki, K. Gałkowski and E. Rogers

In the paper a Java based toolbox has been presented. It is used in teaching of a special case of nD systems - Linear Repetitive Processes (LRP). Its predecessor has been developed in the Matlab environment so to use it a Matlab licence is necessary. This restriction has been removed after making it available in the Internet network as a Java based program. Now a student may click a proper link on a web page and hence start an interactive work with a simulator of LRP. He or she may define a model as well as initial / boundary conditions, then simulate a process as a continuous or discrete case, analyze the results in graphical or numerical form, modify visualization parameters of the plots and finally print the results. In the paper an overview of the tool has been given.

ELECTRONIC AUTOMOTIVE REQUIREMENT DESIGN SPACE - A Bird’s Eye View of a Strategic Requirement Design Space Exploration

Liliana Díaz-Olavarrieta and David Báez-López

The purpose of this article is to make a holistic compilation of many different types of requirements for an automotive electronic communications / control network, and organize them into an easily reusable framework to help with the completeness / strategic consistency issues in the requirement specification process. The requirements framework proposed in this paper aims to answer the question: “What is the requirements design space for an automotive electronic communications network?”, and help in the completeness of the requirements specification through a holistic, multi-perspective, Bird’s Eye View. The main perspectives that will be examined in this requirements design space exploration are: a) Those derived from the “Nature of the User” perspective, b) Those derived from the “Nature of the Application” perspective: Distributed, Real time, Safety-Critical applications, and Resource Constraints requirements, c) Those derived from the “Nature of the Industry” competitive environment: Suppliers, Substitute Products / Technologies, Competitors, and Potential Entrants, the Company itself, its Clients and finally, d) Those derived from the “Nature of the Process Development” perspective, in particular, the component based development (CBD) process of Electronic Subsystem Design within Automotive Companies: component architecting, component assembly and component provisioning. The conceptual domain for the design of these specifications is the area of automotive electronic subsystems (known to be heterogeneous, distributed, real-time systems which in some cases have to implement safety-critical applications requiring fault-tolerant implementations), though the framework is in itself more generally applicable. The design and implementation of heterogeneous, real-time, distributed systems is a complex, knowledge intensive, problem. The design of embedded electronic distributed real-time systems for automotive applications, even more so. Indeed, the complexity comes not only from the electronics, but from all the non-electronic automotive parts which we currently view as “the car” – which interact with, constrain, and impact the electronic systems. The complexity can be handled by a variety of techniques, such as separation of concerns, layering and incremental development, iterative virtual modeling and simulation, and the use of validated automated design processes (such as the A, V models used in the automotive industry) to pass from one design/implementation phase to the next. Designs are generally validated against a set of specifications, both by testing of a system –both of its subsystems parts and their integration- (which is becoming more and more difficult in heterogeneous systems and later in the implementation process), or by following a design-rule constrained “refinement of specifications” within the Component Based Development paradigm that automotive manufacturers usually follow (due to outsourcing and supplier heterogeneity of mechanic, hydraulic and electronic subsystems). In order for the implementation to be correct, not only do the component subsystems have to be correct, the subsystem integration has to be correct and free of unintended interactions. The use of automated design tools starts after the specification or set of requirements for a system / subsystem have been decided upon. Thus, the issue of specification completeness, correctness, and consistency has to be dealt with, separately. The issue of correctness of the specification should be dealt with formal validation models. The issue of consistency can be handled through domain expert specification reviews. The lack of completeness of specifications is a “design specification flaw” which is difficult to detect, unless there is a reference model that one can use (i.e., we know that all states in a binary FSM must have 2 transitions defined –one for a “1” input and another for a “0”, and this knowledge can help to avoid specification flaws where some transition has not been defined). By analogy, if we do not have a higher level “requirements reference meta-model” (to tell us “all the requirements that you could ever think of specifying and don’t want to forget to consider”) we cannot know if the specification is complete. This paper proposes a novel “requirements meta-model” to explore the requirements design space.

EVOLUTIONARY COMPUTATION FOR DISCRETE AND CONTINUOUS TIME OPTIMAL CONTROL PROBLEMS

Yechiel Crispin

Nonlinear discrete time and continuous time optimal control problems with terminal constraints are solved using a new evolutionary approach which seeks the control history directly by evolutionary computation. Unlike methods that use the first order necessary conditions to determine the optimum, the main advantage of the present method is that it does not require the development of a Hamiltonian formulation and consequently, it eliminates the requirement to solve the adjoint problem which usually leads to a difficult two-point boundary value problem. The method is verified on two benchmark problems. The first problem is the discrete time velocity direction programming problem with the effects of gravity, thrust and drag and a terminal constraint on the final vertical position. The second problem is a continuous time optimal control problem in rocket dynamics, the Goddard's problem. The solutions of both problems compared favorably with published results based on gradient and nonlinear programming methods .

EFFICIENT LINEAR APPROXIMATIONS TO STOCHASTIC VEHICULAR COLLISION-AVOIDANCE PROBLEMS

Dmitri Dolgov and Ken Laberteaux

The key components of an intelligent vehicular collision-avoidance system are: sensing, evaluation, and decision making. We focus on the latter task of finding (approximately) optimal collision-avoidance control policies, which can naturally be modeled as a Markov decision process. Unfortunately, the exact classical MDP models and solution methods scale exponentially with the number of environment features, rendering them completely impractical for large-scale real-life domains. To address this, factored MDP representations and approximate solution algorithms have been proposed. In this work we apply approximate linear programming (ALP) to collision-avoidance problems, modeled as factored MDPs. Unlike the commonly-used primal ALP algorithms that approximate only the value function of the MDP, we investigate a composite approach that approximates both the objective function and the feasible region of the linear programs. Our empirical analysis demonstrates that we can obtain high-quality approximations to optimal control policies, while enjoying an exponential reduction in complexity (allowing us to solve problems whose complexity exceeds those solvable by standard MDP methods by tens of orders of magnitude).

ROBUST ILC DESIGN USING MÖBIUS TRANSFORMATIONS

C. T. Freeman, P. L. Lewin and E. Rogers

In this paper a general ILC algorithm is examined and it is found that the filters involved can be selected to satisfy frequency-wise uncertainty limits on the plant model. The probability of the plant model being at a given point in the uncertainty space is specified, and the filters are then chosen to maximise the convergence rate that can be expected in practice. The magnitude of the change in input over successive trials and the residual error have also been encorporated into the cost function. Experimental results are presented using a non-minimum phase test facility to show the effectiveness of the design method.

COOPERATIVE SELF-ORGANIZATION TO DESIGN ROBUST AND ADAPTIVE COLLECTIVES

Gauthier Picard and Marie-Pierre Gleizes

This paper aims at highlighting the benefits of using cooperation as the engine of adaptation and robustness for multi-agent systems. Our work is based on the AMAS (Adaptive Multi-Agent System) approach which considers cooperation as a self-organization mechanism to obtain adequate emergent global behaviors for systems coupled with complex and dynamic environments. A multi-robot resource transportation task illustrates the instantiation of a cooperative agent model equiped with both reactive and anticipative cooperation rules. Various experiments underline the relevance of this approach in dif?cult static or dynamic environments.

ON TEMPORAL DIFFERENCE ALGORITHMS FOR CONTINUOUS SYSTEMS

Alexandre Donzé

This article proposes a general, intuitive and rigorous framework for designing temporal differences algorithms to solve optimal control problems in continuous time and space. Within this framework, we derive a version of the classical TD($\lambda$) algorithm as well as a new TD algorithm which is similar, but designed to be more accurate and to converge as fast as TD($\lambda$) for the best values of $\lambda$, without the burden of finding these values.

REMOTE CONTROL FACILITIES OF WEB-BASED SURVEILLANCE SYSTEM FOR ELECTRIC POWER APPLIANCE AND NETWORK CAMERA

Yoshiro Imai, Yuichi Sugiue, Akira Andatsu, Daisuke Yamane, Hirofumi Kuwajima, Shin’ich Masuda

We have developed surveillance system, which had been organized with network cameras, an integrated web/mail server, mobile computing devices as GUI and remote control devices. Several kinds of devices can be used as our clients including, for example, high-performance cellular phone, which are equipped with Java virtual machine and web-browsing facilities. Our integrated server is designed to play intensive roles of web, e-mail and control services. It can obtain JPEG images from network cameras, process them, accumulate them into its database. It can also receive some types of requests from several kinds of clients, analyze them and perform already assigned services for monitoring and/or controlling. Almost all software of our surveillance system have been written in Java programming language, because of easy and powerful description of GUI as well as network programming. Users of our system can utilize remote monitoring and controlling anywhere and anytime, by means of mobile computing devices. Our integrated server can analyze the request from clients, generate the specific signals to subserver to switch several appliances as well as network cameras. Its subserver is able to control appliance power switching through the power line network, while its network cameras can be controlled by means of homing facilities of cameras themselves. With these facilities, remote control of electric power appliances and network cameras can be achieved by means of the commands from the above-mentioned integrated server.

A CONTROL SYSTEM USING BEHAVIOUR HIERARCHIES AND NEURO-FUZZY APPROACH

Dilek Arslan and Ferda N Alpaslan

In agent-based systems, especially in autonomous mobile robots, modelling the environment and its changes is a source of problems. It is not always possible to effectively model the uncertainty and the dynamic changes in complex, real-world domains. Control systems must be robust to changes and must be able to handle the uncertainties to overcome this problem. In this study, a reactive behaviour based agent control system is modelled and implemented. The control system is tested in a navigation task using an environment, which has randomly placed obstacles and a goal position to simulate an environment similar to an autonomous robot’s indoor environment. Then the control system was extended to control an agent in a multi-agent environment. The main motivation of this study is to design a control system, which is robust to errors and is easy to modify. Behaviour based approach with the advantages of fuzzy reasoning systems is used in the system

A NEW METHOD FOR WEIGHT UPDATING IN FUZZY COGNITIVE MAPS USING SYSTEM FEEDBACK

Theodore L. Kottas, Yiannis S. Boutalis and Manolis A. Christodoulou

Fuzzy Cognitive Maps (FCMs) have found many applications in social -fnancial -political problems. In this paper we propose a method of FCM operation, which can be used to represent and control any real system, including traditional electro-mechanical systems. In the proposed approach the FCM reaches its equilibrium point using direct feedback from the node values of the real system and the limitations imposed by the control objectives for the node values of the system. The experts’ knowledge, which is represented in the weights of the nodes’ interconnections, undergoes a continuous on-line adaptation based on feedback from the real system. An algorithm for weight updating is proposed, which is based on system feedback and which includes specially designed matrices that lead the FCM and consequently the real system associated with it in a balanced equilibrium state. The proposed methodology is tested by simulating the operation of a hydro-electric plant.

SPATIAL APPROACH IN RIVER BASIN MANAGEMENT USING DECISION MAKING STRATEGIES

Christian Menard

In this paper an approach towards a spatial decision support system is proposed for optimizing the management of river basins. All data from monitoring stations are collected and stored in a centralized database system. Since all measurement data are spatial and time related, spatial services fulfill the requirements in a decision making process best. A spatial decision support system approach is presented in which modeling is based on a network structure. This network can then be used to design and calibrate the underlying model. Spatial information can be obtained directly using GIS functionality.

STATIONARY FULLY PROBABILISTIC CONTROL DESIGN

Tatiana V. Guy and Miroslav Kárný

Stochastic control design chooses the controller that makes the closed-loop system behavior as close as possible to the desired one. The considered fully probabilistic design employs probabilistic description of both closed-loop and desired behaviors and uses Kullback-Leibler divergence as their proximity measure. An explicit minimiser provided by this design allows simpler approximation of analytically non-feasible cases. The existing formulations are oriented towards finite-horizon design and lead to the non-stationary optimal strategy. The paper provides infinite-horizon problem formulation and corresponding solution. This leads to a stationary strategy, which approximation is much easier.

CONTROL FOR ELECTRICAL NEUROMUSCULAR STIMULATOR USING FUZZY LOGIC - Trainning gait in paraplegics

Leonardo Rodrigues da Silva and Percy Nohama

This article presents a personal computer-based control system for an electrical stimulator using fuzzy logic. The input signal comes from a goniometer and the output is the stimulation level to be applied in the muscle of the patient. By this way, that control system is made for the therapist that just specifies the desired joint angle. The movement that the patient will execute can be mimicked from a person with normal movements, storing his or her joint’s angles during the execution of some task, and later reproducing it in the person without the voluntary movements. Such movements will be more proper of a human than a planned execution of a computational system, which the movement is structuralized by means of vectors, angles and times placed of supposed form.

MILITARY VEHICLE TYPE CLASSIFICATION - Intelligent Control Systems and Optimization

Jerzy Jackowski

This work presents the results of the measurement of the noise generated by vehicles differentiated in respect of the vehicle weight and structure. The analysis of registered acoustic signals was carried out on the basis of their frequency representation. Based on the Student difference test, a series of parameters of determined spectral signal power densities were examined for their usefulness for a differentiating feature vector. A process of qualifying a registered signal of a detected object to a proper class can be realized by various methods. Most often it is carried out on the basis of the object feature vector position against surfaces separating it from the vectors of other objects in the multidimensional space of features. Meeting the requirement of maximum classifier structure simplification, searching for the best separating plane was limited to the neuron network method based on the Rosenblatt perceptron education. Specification of measurement results indicates that there is a high probability of correct recognition of acoustic signals generated by the wheel and caterpillar vehicle motion.

REAL-TIME TIME-OPTIMAL CONTROL FOR A NONLINEAR CONTAINER CRANE USING A NEURAL NETWORK

T. J. J. van den Boom, J. B. Klaassens and R. Meiland

This paper considers time-optimal control for a container crane based on a Model Predictive Control approach. The model we use is nonlinear and it is planar, i.e. we only consider the swing (not the skew) and we take constraints on the input signal into consideration. Since the time required for the optimization makes time-optimal not suitable for fast systems and/or complex systems, such as the crane system we consider, we propose an off-line computation of the control law by using a neural network. After the neural network has been trained off-line, it can then be used in an on-line mode as a feedback control strategy.

INTELLIGENT MOBILE MULTI-ROBOTIC SYSTEMS: SOME CHALLENGES AND POSSIBLE SOLUTIONS

Flávio S. Corrêa da Silva, Renata Wassermann, Ana Cristina V. Melo, Leliane N. Barros and Marcelo Finger

Intelligent mobile multi-robotic systems (IMMRSs) are coordinated systems of autonomous mobile robots endowed with reasoning capabilities. This sort of systems requires the integrated application of a variety of state-of-the-art techniques developed within the realm of Artificial Intelligence, as well as instigates the further development of different specialisations of Artificial Intelligence. In the present article we examine some of these techniques and specialisations, discuss some specific challenges proposed to the field of Artificial Intelligence by IMMRSs, and suggest possible solutions to these challenges. In order to make our presentation more concrete, we employ throughout the article a specific example of IMMRS application, namely security surveillance of an empty building by a team of robots - the well known pursuit-evasion problem.

PEDESTRIAN RECOGNITION FOR INTELLIGENT TRANSPORTATION SYSTEMS

D. Fernández, I. Parra, M. A. Sotelo, L. M. Bergasa, P. Revenga, J. Nuevo and M. Ocaña

This paper describes a binocular vision-based pedestrian recognition System. The basic components of pedestrians are first located in the image and then combined with a SVM-based classifier. This poses the problem of pedestrian detection and recognition in real, cluttered road images. Candidate pedestrians are located using a subtractive clustering attention mechanism. A distributed learning approach is proposed in order to better deal with pedestrians variability, illumination conditions, partial occlusions and rotations. The performance of the pedestrian recognition system is enhanced by a multiframe validation process. By doing so, the detection rate is largely increased. A database containing hundreds of pedestrian examples extracted from real traffic images has been created for learning purposes. We present and discuss the results achieved up to date.

EXTENSION VERSUS BENDING FOR CONTINUUM ROBOTS

Robin McDonnell, George Grimes, Ian D. Walker and Carlos Carreras

In this paper, we analyze the capabilities of a novel class of continuous-backbone (“continuum”) robots. These robots are inspired by biological “trunks, and tentacles”. However, the capabilities of established continuum robot designs, which feature controlled bending but not extension, fall short of those of their biological counterparts. In this paper, we argue that the addition of controlled extension provides dual and complementary functionality, and correspondingly enhanced performance, in continuum robots. We present an interval-based analysis to show how the inclusion of controllable extension significantly enhances the workspace and capabilities of continuum robots.

COMPOSITIONAL ANALYSIS FOR REGULARITY, LIVENESS AND BOUNDEDNESS

Li Jiao

Desel introduced regular nets by the linear algebraic representation of nets [1]. Regularity is a sufficient condition for an ordinary net to be live and bounded. All the conditions checking the regularity of a net are decidable in polynomial time in the size of a net [2]. This paper proves that regularity, liveness and boundedness can be preserved after many compositional operations. This means that one designer can construct complex nets satisfying regularity, liveness and boundedness properties from simpler ones without forward analysis.

TOPOLOGICALLY ROBUST RECONSTRUCTION OF A 3D OBJECT WITH ORGANIZED MESHING

Junta Doi and Wataru Sato

This study proposes a topologically robust and noise-resistive reconstruction procedure that approximates a real 3D object. A geometric model with desired meshing is directly reconstructed based on a solid modeling approach. The radial distance of each scanning point from the axis of the cylindrical coordinates is measured using a laser triangulation sensor. The angular and vertical positions of the laser beam are two other coordinate values of the modeling. A face (mesh) array listing (topology), which defines the sampling point connectivity to form the mesh and the shape of the mesh, is assigned to meet the meshing The topologically stable and thus organized meshing, and hence, an accurate approximation is then accomplished. It is free from the noise-originated misconnection and shape ambiguity, which is unavoidable in the recent ICP (Iterative Closed Point) modeling. This proposal allows a versatile 3D shape processing and modification, for instance, for cultural heritage retrieval and virtual training.

A NEW ART GALLERY ALGORITHM FOR SENSOR LOCATION

Andrea Bottino and Aldo Laurentini

Locating sensors in2D can be often modeled as an Art Gallery problem. Unfortunately, this problem is NP-hard, and no finite algorithm, even exponential, is known for its solution. Algorithms able to closely approximate the optimal solution and computationally feasible in the worst case are unlikely to exist. However this is an important problem, and algorithms with “good” performance in practical cases are sorely needed. After reviewing the available algorithms, we propose a new sensors location incremental technique. The technique converges toward the optimal solution. It locally refines a starting approximation provided by an integer covering algorithm, where each edge is observed entirely by at least one sensor. A lower bound for the number of sensor, specific of the polygon considered, is used for halting the algorithm, and a set of rules are provided to simplify the problem.

AN OPTIMAL CONTROL SCHEME FOR A DRIVING SIMULATOR

Hatem Elloumi, Marc Bordier and Nadia Maïzi

Within the framework of driving simulation, control is a key issue to providing the driver realistic motion cues. Visual stimulus (virtual reality scene) and inertial stimulus (platform motion) induce a self-motion illusion. The challenge is to provide the driver with the sensations he would feel in real car maneuvering. This is an original control problem. Indeed, the first goal is not classical path tracking but fooling the driver awareness. Constrained workspace is the second issue classically addressed by motion cueing algorithms. The purpose of this paper is to extend the works of Telban and Cardullo on the optimal motion cueing algorithm. A nonlinear dynamical model of the robot is brought in. The actuator forces are directly included in the optimal control scheme. Consequently a better (global) optimization and an advanced parametrization of the control are achieved.

STATE TRANSFORMATION FOR EULER-LAGRANGE SYSTEMS

M. Mabrouk and J. C. Vivalda

The transformation of an Euler-Lagrange system into a state affine system in order to solve some interesting problem as the design of observer, the output tracking control, is considered in this paper. A necessary and a sufficient condition is given as well as a method to compute this transformation.

IMPROVEMENT OF THE DYNAMICS OF THE CONTINUOUS LINEAR SYSTEMS WITH CONSTRAINTS CONTROL

N.H. Mejhed, A. Hmamed and A. Benzaouia

A time varying control law is proposed for linear continuous-time systems with non Symmetrical constrained control. Necessary and sufficient conditions allowing us to obtain the largest non-symmetrical positively invariant polyhedral set with respect to (w.r.t) the system in the closed loop are given. The asymptotic stability of the origin is also guaranteed. The case of symmetrical constrained control is obtained as a particular case. The performances of our regulator with respect to the results of [3] are shown with the help of an example

PATH FOLLOWING IN UNKNOWN ENVIRONMENT FOR A CAR-LIKE MOBILE ROBOT

Niramon Ruangpayoongsak and Hubert Roth

The path following is the automatic control of the mobile robot movement along the specified path without human interference. The proposed path following applies for the robot navigation in unknown environments, where the robot has no preliminary information about obstacles. An innovative idea for the path following control is to integrate the basic path following control with the obstacle avoidance and the trajectory generation. The basic path following control is first implemented without obstacle detection. The robot receives the desired path, performs driving along the path, and stops at the destination. The obstacle avoidance is developed by wall following technique using on ultrasonic and infrared sensors. The trajectory generation is to generate the fittest trajectory between current position and the destination position after the robot is free from obstacles. These algorithms base on the car manoeuvring characteristics.

THERMAL SPRAYING ROBOT KINEMATICS AND LASER PATTERN CONTROL

Dermot Breen, Eugene Coyle and David Kennedy

The thermal spraying surface engineering industry relies on manual spraying and standard pre-programmed robotic systems. This research presents the completed geometric forward and inverse kinematics solution for a non standard articulated robotic manipulator which includes continuous 3600 axis rotation for waist, shoulder and elbow joints with a commercially available joint for tilt and pitch. The research also details the use of PTFE electroless nickel slip rings and brushes for providing delivery of power and data through the 3600 continuous rotation joints. The automatic analysis of distance and orientation measurement via a pattern producing laser and camera system are described which can be applied to the thermal spraying process for automatic feedback control of the robotic arm manipulator. The competed technical and simulation design will provide for the automatic application of advanced surface coatings to enhance wear, low friction and corrosion resistance properties to substrates via a thermal spraying process.

PREDICTIVE CONTROL FOR MODERN INDUSTRIAL ROBOTS - Algorithms and their applications

Květoslav Belda, Josef Böhm and Pavel Píša