This paper proposes a novel and efficient global intelligent digital redesign technique for a Takagi-Sugeno (T-S) fuzzy system. The term of intelligent digital redesign involves converting an existing analog fuzzy-model-based controller into an equivalent digital counterpart in the sense of state-matching. The proposed method should be notably discriminated from the previous works in that it allows us to globally match the states of the overall closed-loop T-S fuzzy system with the predesigned analog fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller, and further to examine the stabilizability by the redesigned controller in the sense of Lyapunov . The key idea is that the global intelligent digital redesign problem is viewed as a convex minimization problem of the norm distance between nonlinearly interpolated linear operators to be matched. Sufficient conditions for the global state-matching and the stability of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). A complex nonlinear system, Duffing-like chaotic oscillator is simulated and demonstrated to validate the feasibility and effectiveness of the proposed digital redesign technique, which implies the safe applicability to the digital control system.
Bibliographical noteFunding Information:
Manuscript received December 14, 2001; revised April 21, 2003. This work was supported by KOSEF under Grant R01-2001-000-00316. H. J. Lee, H. Kim, and J. B. Park are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea (e-mail: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org). Y. H. Joo is with the School of Electronic and Information Engineering, Kunsan National University, Chonbuk 573-701, Korea (e-mail: email@example.com). W. Chang is with the Samsung Advanced Institute of Technology, Suwon 440-600, Korea (e-mail: firstname.lastname@example.org). Digital Object Identifier 10.1109/TFUZZ.2003.819826
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics