Intelligent Digital Redesign for Sampled-data Fuzzy Control Systems Based on State-matching Error Cost Function Approach

Abstract

This paper presents a novel intelligent digital redesign (IDR) technique for a sampled-data fuzzy controller of fuzzy systems which can be modeled in Takagi-Sugeno (T-S) fuzzy systems. The IDR technique, which is one of sampled-data fuzzy controller design techniques, is to effectively convert a well-designed analog fuzzy controller into a sampled-data fuzzy controller in the state-matching sense. In this paper, unlike previous IDR techniques, the state-matching error is minimized by defining the state-matching error cost function. Also, to improve the state-matching condition, the exact discretized model is used for the proposed IDR technique. The sufficient condition of the proposed IDR technique is developed and derived in terms of linear matrix inequalities (LMIs). Finally, some numerical examples are provided to verify the effectiveness of the proposed techniques.