篇名 | On-line Adaptive Interval Type-2 Fuzzy Controller Design via Stable SPSA Learning Mechanism |
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卷期 | 14:4 |
作者 | Ching-Hung Lee 、 Feng-Yu Chang 、 Chih-Min Lin |
頁次 | 489-500 |
關鍵字 | interval type-2 fuzzy neural system;uncertainty bounds 、 simultaneous perturbation stochastic approximation algorithm 、 Lyapunov theorem 、 on-line control 、 EI 、 SCI 、 SCIE 、 Scopus |
出刊日期 | 201212 |
This paper proposes an interval type-2 Takagi-Sugeno-Kang fuzzy neural system (IT2TFNS) to develop an on-line adaptive controller using stable simultaneous perturbation stochastic approximation (SPSA) algorithm. The proposed IT2TFNS realizes an interval type-2 TSK fuzzy logic system formed by the neural network structure. Differ from the most of interval type-2 fuzzy systems, the type-reduction of the proposed IT2TFNS is embedded in the network by using uncertainty bounds method such that the time-consuming Karnik-Mendel (KM) algorithm is replaced. The proposed stable SPSA algorithm provides the gradient free property and faster convergence. However, the stable SPSA algorithm inherently has the problem for on-line adaptive control. Hence, in order to achieve the on-line result, we utilize the sliding surface to develop a new on-line adaptive control scheme. In addition, the corresponding stable learning is derived by Lyapunov theorem which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results are shown to demonstrate the performance and effectiveness of our approach.