Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm

Abstract

A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an online fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors x_p, x_i, and x_d are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead’s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.

Keywords

optimal / fuzzy inference / PID controller / adjustable factor / flexible polyhedron search algorithm / intelligent artificial leg

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