Robot arm fuzzy control by a neuro-genetic algorithm

Robot arm control is a difficult problem. Fuzzy controllers have been applied succesfully to this control task. However, the definition of the rule base and the membership functions is itself a big problem. In this paper, an extension of a previously proposed algorithm based on neuro-genetic techniques is introduced and evaluated in a robot arm control problem. The extended algorithm can be used to generate a complete fuzzy rule base from scratch, and to define the number and shape of the membership functions of the output variables. However, in most control tasks, there are some rules and some membership functions that are obvious and can be defined manually. The algorithm can be used to extend this minimal set of fuzzy rules and membership functions, by adding new rules and new membership functions as needed. A neural network based algorithm can then be used to enhance the quality of the fuzzy controllers, by fine tuning the membership functions. The approach was evaluated in control tasks by using a robot emulator of a Philips Puma like robot called OSCAR. The fuzzy controllers generated showed to be very effective to control the arm. A complete graphical development system, together with the emulator and examples is available in Internet.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Prior knowledge in evolutionary fuzzy recurrent controllers design

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI

Evolution of recurrent fuzzy controllers

The main advantage of a recurrent architecture is the ability to store information from prior system states. A recurrent fuzzy controller includes hidden fuzzy variables which makes the controller more appropriate to deal with dynamic systems. We are currently investigating the effect of evolution of recurrent fuzzy controllers by applying the FV representation, which provides a set of advantages that can signi catively benefit the quality of the knowledge insertion process.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

Evolution of Voronoi based Fuzzy Recurrent Controllers

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. Among the most successful methods to automate the fuzzy controllers development process are evolutionary algorithms. In this work, we propose the Recurrent Fuzzy Voronoi (RFV) model, a representation for recurrent fuzzy systems. It is an extension of the FV model proposed by Kavka and Schoenauer that extends the application domain to include temporal problems. The FV model is a representation for fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, fulfilling the $\epsilon$-completeness property and providing a simple way to introduce a priory knowledge. In the proposed representation, the temporal relations are embedded by including internal units that provide feedback by connecting outputs to inputs. These internal units act as memory elements. In the RFV model, the semantic of the internal units can be specified together with the a priori rules. The geometric interpretation of the rules allows the use of geometric variational operators during the evolution. The representation and the algorithms are validated in two problems in the area of system identification and evolutionary robotics

Evolution of neurocontrollers in changing environments

One of the most challenging aspects of the control theory is the design and implementation of controllers that can deal with changing environments, i. e., non stationary systems. Quite good progress has been made on this area by using different kind of models: neural networks, fuzzy systems, evolutionary algorithms, etc. Our approach consists in the use of a memory based evolutionary algorithm, specially designed in such a way that can be used to evolve neurocontrollers to be applied in changing environments. In this paper, we describe our architecture, and present an example of its application to a typical control problem.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

An architecture for fuzzy logic controllers evolution and learning in microcontrollers based environments

The growing number of control models based on combinations of neural networks, fuzzy systems and evolutionary algorithms shows that they represent a flexible and powerful approach. However, most of these models assume that there is enough CPU power for the evolutionary and learning algorithms, which in a large number of cases is an unrealistic assumption. It is usual that the control tasks are performed by small microcontrollers, which are very near to or embedded in the plant, with low power, low cost and dedicated to a single task. This work proposes an architecture for evolution and learning in adaptive control, specifically designed to operate in microcontrollers based environments. An evaluation on a simulated temperature control environment is provided, together with details on the current hardware implementation.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

A low communication overhead parallel implementation of the back-propagation algorithm

The back-propagation algorithm is one of the most widely used training algorithms for neural networks. The training phase of a multilayer perceptron by using this algorithm can take very long time making neural networks difficult to accept. One approach to solve this problem consists in the parallelization of the training algorithm. There exists many different approaches, however most of them are well adapted to specialized hardware. The idea to use a network of workstations as a general purpose parallel computer is widely accepted. However, the communication overhead imposes restrictions in the design of parallel algorithms. In this work, we propose a parallel implementation of the back-propagation algorithm that is suitable to be applied to a network of workstations. The objective is twofold. The first goal is to increment the performance of the training phase of the algorithm with low communication overhead. The second goal is to provide a dynamic assignment of tasks to processors in order to make the best use of the computational resources.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Multi-disciplinary Optimization with Standard Co-simulation Interfaces

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The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony (AC) model for the Multiple Knapsack Problem (MKP). The ant colony metaphor, as well as other evolutionary metaphors, was applied successfully to diverse heavily constrained problems. An AC system is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an AC system is adapted to the MKP. We present some results regarding its performance against known optimum for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

The ant colony metaphor for multiple knapsack problem

This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Facultad de Informátic