Nonlinear discrete-time controller with unknown systems identification based on fuzzy rules emulated network

被引:9
|
作者
Treesatayapun, Chidentree [1 ,2 ]
机构
[1] CINVESTAV IPN, Dept Robot & Adv Mfg, Unidad Saltillo, Ramos Arizpe 25900, Mexico
[2] N Chiang Mai Univ, Dept Elect Engn, Chiang Mai 50230, Thailand
关键词
Discrete-time; Adaptive control; Nonlinear; Neuro-fuzzy; NEURAL-NETWORKS;
D O I
10.1016/j.asoc.2009.08.005
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article introduces an adaptive controller for a class of unknown nonlinear discrete-time systems based on multi-input fuzzy rules emulated network (MIFREN). By the estimation of any nonlinear systems from MIFREN, this network is assigned to identify the unknown system under control. The proposed control law is introduced by the result of nonlinear system identification based on MIFREM and the defined sliding condition. Without the need of any off-line learning phase, all control parameters including the learning rate for MIFREN are selected to guarantee the bonded signals such as the model error, tuned weight vector, the tracking error and the sliding signal via the defined Lyapunov functions and proposed theorems. The performance of the proposed control algorithm is demonstrated and the main theorem is validated by computer simulation results. (C) 2009 Elsevier B.V. All rights reserved.
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页码:390 / 397
页数:8
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