Name
Title | ||
|---|---|---|
Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach. |
Abstract | ||
|---|---|---|
This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSMCB.2010.2065801 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
uncertain nonlinear systems,uncertain nonlinear system,robust adaptive control,uncertain systems,t-s fuzzy-neural model,fuzzy-neural modeling approach,online identification algorithm,nonaffine nonlinear system,nonlinear function,traditional t-s fuzzy control,control system synthesis,closed loop system,robust control,robust adaptive controller design,adaptive t-s fuzzy-neural controller,online modeling,uncertain function,virtual linearized system,complicated uncertain nonlinear system,uncertain system,closed-loop system,adaptive control,nonlinear control systems,lyapunov theory,tracking,fuzzy-neural model,takagi-sugeno fuzzy neural model,robust tracking controller design,closed loop systems,fuzzy neural nets,lyapunov methods,online t-s fuzzy neural modeling,adaptive systems,robustness,nonlinear system,nonlinear systems,nonlinear dynamics,algorithms,mathematical model,computer simulation,adaptive system,feedback,control systems,mimo | Lyapunov function,Mathematical optimization,Nonlinear system,Computer science,Control theory,Adaptive system,Robustness (computer science),Adaptive control,Control system,Fuzzy control system,Robust control | Journal |
Volume | Issue | ISSN |
41 | 2 | 1941-0492 |
Citations | PageRank | References |
28 | 1.01 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Hsing Chien | 1 | 89 | 8.33 |
| Wei-Yen Wang | 2 | 995 | 87.40 |
| Yih-Guang Leu | 3 | 515 | 33.76 |
| Tsu-Tian Lee | 4 | 1635 | 148.07 |