Fuzzy and evolutionary modelling of nonlinear control systems

The development of a controller for a nonlinear system is still a challengi ng task for control engineers. This paper presents a method for the optimiz ation of a Fuzzy Logic System (FLS) [1) for the control of nonlinear system s. A fine-grained parallel genetic algorithm has been proposed to identify the parameters of the FLS. The proposed method has been applied to control a popular set of benchmark problems, i.e., an inverse pendulum with both co nstant and varying shaft length and a couple of unjoined inverse pendulums fixed on a single platform. It is argued that, because of its ability to ca pture the imprecise information that humans can understand very easily in n atural language, a fuzzy logic system provides an ideal general frame of re ference for modelling any nonlinear system involving uncertainties. In this context, the evolutionary algorithms with their parallel power to search t hrough multidimensional space are effective in estimating the parameters of the fuzzy logic system. The fine-grained parallel genetic algorithm has be en executed on a PC-hosted 16-node transputer platform running under the He llos operating system. The quantitative comparison of the fuzzy-evolutionar y controller to the LQR controller has been given for one example system, w hile for the other two systems (for which there are no analytical solutions ) the value of the objective function has been provided for future referenc e. (C) 2001 Elsevier Science Ltd. All rights reserved.