Cell state space based incremental best estimate directed search algorithm for robust fuzzy logic controller optimization with multi-model concept

This paper presents a new version of cell state space based incremental best estimate directed search (IBEDS) algorithm with multi-model concept for robust Takagi-Sugeno type-fuzzy logic controller (FLC) automatic optimization. Typically, IBEDS starts with an initial training set, and a FLC with randomly initialized parameters is trained in an iterative procedure by least mean square algorithm. The optimized FLC may not be robust. This paper proposes a new version of IBEDS that can incorporate robustness information into the training set. First, several models are established to represent model uncertainty, parameter fluctuation, etc., then in each iteration of IBEDS, a trained FLC is evaluated on all models, the control commands with the worst local performance for all models will be used to update the training set. A 2D and a 4D inverted pendulums are studied. It is shown that with multi-model concept and IBEDS, computational design of robust FLC can be done efficiently even for high order systems