Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators

Authors:
Weisheng Chen;L. C. Jiao;Jianshe Wu
Affiliations:
Xidian University, Department of Applied Mathematics and the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, 710071, Xi’an, China;Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, 710071, Xi’an, China;Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, 710071, Xi’an, China
Venue:
Neural Computing and Applications - Special Issue on Theory and applications of swarm intelligence
Year:
2012

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Global tracking control of a wheeled mobile robot using RBF neural networks

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Globally stable direct adaptive backstepping NN control for uncertain nonlinear strict-feedback systems

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Abstract

In previous adaptive neural network control schemes, neural networks are usually used as feedback compensators. So, only semi-globally uniformly ultimate boundedness of closed-loop systems can be guaranteed, and no methods are given to determine the neural network approximation domain. However, in this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the globally uniformly ultimate boundedness of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks. Simulation examples are given to illustrate the effectiveness of the proposed control approaches.