Neuro-fuzzy techniques for intelligent control

Abstract

In this study the utilization of neuro-fuzzy techniques is investigated for the realisation of intelligent control. Neuro-fuzzy systems combine the learning capabilities of neural networks with the rule based system description offered by fuzzy logic. Techniques are evaluated by means of a simple two-dimensional simulation of a three-segment robotic manipulator. The inverse kinematics and path planning required in such a system provides all the complexity needed for testing and evaluation. In complex systems, obtaining sufficient training examples also prove to be a problem. To address this problem an automated process of 'action evolution' was implemented, through which a genetic algorithm is used to collect examples as training data. A generic modular controller architecture is developed in order to simplify the comparison of different neural and neuro-fuzzy controllers. This architecture unifies numerous controller architectures into a single controller, capable of representing and combining classical, adaptive, intelligent and reinforcement learning controllers and it exposes the presence of various cognitive attributes. Neural and neuro-fuzzy systems are implemented and evaluated for local trajectory tracking and for global path planning. A serious problem of contradicting solutions encountered in the examples produced by the genetic algorithm, is solved through reinforcement learning. A modified fuzzy clustering algorithm is used to estimate the system's state values and control commands are derived by optimising this value. Modifications included negative reinforcement of prohibited states and concepts borrowed from ant algorithms for establishing solution paths. This study highlights the effect of ill-posed problems on the training of intelligent controllers. It shows how the problem can be simplified by basing the control policy on a value function and it implements neuro-fuzzy techniques to rapidly construct such a function. Proposals are made on how memory based search algorithms can be used to improve training data integrity and how evolving self-organising maps might prevent erroneous policy interpolation. This study contributes valuable conclusions on the implementation of intelligent controllers for the control of complex non-linear systems.