Analysis and synthesis of discrete nonlinear passive systems via affine TS fuzzy models

International Journal of Systems Science (Impact Factor: 2.1). 08/2008; 39(8):809-821. DOI: 10.1080/00207720801902580
Source: DBLP

ABSTRACT The article considers the analysis and synthesis problem for the discrete nonlinear systems, which are represented by the discrete affine Takagi–Sugeno (T–S) fuzzy models. The state feedback fuzzy controller design methodology is developed to guarantee that the affine T–S fuzzy models achieve Lyapunov stability and strict input passivity. In order to find a suitable fuzzy controller, an Iterative Linear Matrix Inequality (ILMI) algorithm is employed in this article to solve the stability conditions for the closed-loop affine T–S fuzzy models. Finally, the application of the proposed fuzzy controller design methodology is manifested via a numerical example with computer simulations.

Download full-text


Available from: Wei Chang, Sep 29, 2015
24 Reads
  • Source
    • "Cao and Frank (2001), Guan and Chen (2004), Chen and Liu (2005), Lin, Wang, and Lee (2005), Lin, Wang, Lee, and He (2006), Hsiao (2007), Jun (2007), Zhang, Xu, Zang, and Zou (2007), Chu, Tsai, and Chang (2008) "
    [Show abstract] [Hide abstract]
    ABSTRACT: The problem of robust fuzzy control for a class of nonlinear fuzzy impulsive stochastic systems with time-varying delays is investigated. The nonlinear delay system is represented by the well-known T–S fuzzy model. The so-called parallel distributed compensation idea is employed to design the state feedback controller. Sufficient conditions for mean square exponential stability of the closed-loop system are derived in terms of linear matrix inequalities. Finally, a numerical example is given to illustrate the applicability of the theoretical results.
    International Journal of Systems Science 10/2010; 41:1163-1172. DOI:10.1080/00207720903144487 · 2.10 Impact Factor
  • Source
    • "Generally, the strictly input passive type [8] [14] of passivity theory is utilized to achieve the attenuation performance. So, we also use the strictly input passive type for investigating the attenuation performance of system in this paper. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The issue of observer-based robust passive fuzzy controller design is discussed and investigated in this paper for uncertain stochastic Takagi-Sugeno (T-S) fuzzy model with external disturbance. For describing the stochastic behaviors of system, the stochastic differential equation is used to structure the stochastic T-S fuzzy model for representing the nonlinear stochastic systems. Using the Lyapunov function and passivity theory, the sufficient stability condition can be derived in term of linear matrix inequality (LMI) by Ito¿'s formula. With stability conditions, a less conservatism design method is developed to synthesize the state feedback fuzzy controller and fuzzy observer for guaranteeing the asymptotical stability and strictly input passivity of system in the mean square.
    Proceedings of the 48th IEEE Conference on Decision and Control, CDC 2009, combined withe the 28th Chinese Control Conference, December 16-18, 2009, Shanghai, China; 01/2009
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes a design of fuzzy controller for the input constrained discrete nonlinear passive systems via affine Takagi-Sugeno (T-S) fuzzy models. The stability conditions are derived based on Lyapunov and passive theories for the closed-loop system. The fuzzy controller design is accom-plished by using the concept of Parallel Distribution Com-pensation (PDC). According to the proposed fuzzy control ap-proach, the controlled system can achieve passive property and the input constraint, simultaneously. The Iterative Linear Matrix Inequality (ILMI) algorithm is applied in this paper to find the feasible solutions for the proposed fuzzy controller design approach. At last, a numerical simulation to the truck-trailer system is provided to demonstrate the applicability and effectiveness of the proposed fuzzy control methodology.
Show more