Compensatory Adaptive Neural Fuzzy Inference System

Abstract

The traditional approach to fuzzy design is based on knowledge acquired by expert operators formulated into rules. However, operators may not be able to translate their knowledge and experience into a fuzzy logic controller. In addition, most adaptive fuzzy controllers present difficulties in determining appropriate fuzzy rules and appropriate membership functions. This chapter presents adaptive neural-fuzzy controller equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a two degree of freedom robotic system. The simulation results obtained show a fairly high accuracy in terms of position and velocity tracking, what highlights the effectiveness of the proposed controllers.

Online Adaptation of a Compensatory Neuro-Fuzzy Controller Parameters Using the Extended Kalman Filter: Application on an Inverted Pendulum

“…Therefore, combining fuzzy logic with neural networks in the form of a single network structure allows building more efficient neuro-fuzzy controllers, and this by taking advantage of the inference capacity of fuzzy reasoning and computational parallelism of neural networks. Among the most efficient adaptive neuro-fuzzy systems, we cite the CANFIS (Compensatory Adaptive Neuro-Fuzzy Inference System) structure, which consists of using a fuzzy compensator which compensates for the bad choice of membership functions, and which uses techniques which allow us to adjust the dynamics of fuzzy rules, so as to adapt to the environment [5]. However, the use of such an adaptive structure requires a high computation time and its implementation in practice then requires powerful control boards in order to minimize the computational time of the algorithm in order to converge on the desired performance.…”