A general cell state space based TS type fuzzy logic controller automatic rule extraction and parameter optimization algorithm

This paper presents a new cell state space based Takagi-Sugeno (TS) type fuzzy logic controller (FLC) automatic rule extraction and parameter optimization algorithm. A discrete optimal control table (OCT) is first generated under a predefined cost function. The rule base antecedents of a FLC can then be extracted from the OCT with fuzzy clustering techniques. The parameters of the rule output functions are optimized by a training data set in a novel iterative procedure through least mean square (LMS) training algorithm. The initial value of the training data set is the OCT. An OCT is generally very noisy, thus may not train a FLC very well. This is especially true for FLCs of high order systems. A new method is developed to exclude the undesirable control commands inside an OCT. In each parameter optimization iteration, the trained FLC is evaluated with cell state space-based global and local performance measures. The training data set is then updated based on the evaluation. In this way, the training set is optimized in every iteration, and the FLC trained by the set is also optimized progressively. Since the new method makes use of every FLC evaluated, a fast convergence speed is expected. A 4D inverted pendulum is studied to justify the algorithm The new approach is compared favorably with a linear quadratic regulator.