DocumentCode :
2249150
Title :
Deterministic learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems
Author :
Wang, Cong ; Wang, Min
Author_Institution :
Sch. of Autom. & Center for Control & Optimization, South China Univ. of Technol., Guangzhou, China
fYear :
2011
fDate :
17-19 Sept. 2011
Firstpage :
362
Lastpage :
367
Abstract :
This paper studies deterministic learning from adaptive neural control for a class of strict-feedback nonlinear systems with unknown affine terms. Firstly, an ISS-modular approach is presented to ensure uniformly ultimate boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. The proposed ISS-modular approach avoids the possible control singularity without the restriction of the derivative of affine terms. Secondly, it will be shown the proposed stable ISS-modular adaptive neural controller is able to learn closed-loop system dynamics. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using previous methods. To overcome these difficulties, the stable closed-loop system in control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation (PE) condition for radial basis function (RBF) neural networks (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals, and a learning ability is implemented during a closed-loop feedback control process. The learned knowledge can be reused in the same or similar control tasks, and avoid the tremendous repeated training process of NNs.
Keywords :
adaptive control; approximation theory; asymptotic stability; cascade control; closed loop systems; feedback; learning systems; neurocontrollers; nonlinear control systems; time-varying systems; ISS-modular adaptive NN control; LTV perturbed subsystem; RBF neural network; adaptive neural control; cascade structure; closed-loop control system dynamics; closed-loop feedback control; deterministic learning; exponential stability; linear time-varying; nonlinear strict-feedback system; partial persistent excitation; radial basis function; recursive design; state transformation; tracking error; Adaptive systems; Approximation methods; Artificial neural networks; Orbits; Process control; Radial basis function networks; Stability analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems (CIS), 2011 IEEE 5th International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-61284-199-1
Type :
conf
DOI :
10.1109/ICCIS.2011.6070356
Filename :
6070356
Link To Document :
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