Adaptive neural network tracking control of MIMO nonlinear systems with unknown dead zones and control directions

Authors:
Tianping Zhang;Shuzhi Sam Ge
Affiliations:
Department of Automation, College of Information Engineering, Yangzhou University, Yangzhou, China;Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

In this paper, adaptive neural network (NN) tracking control is investigated for a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems in triangular control structure with unknown nonsymmetric dead zones and control directions. The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown control directions. It is shown that the dead-zone output can be represented as a simple linear system with a static time-varying gain and bounded disturbance by introducing characteristic function. By utilizing the integral-type Lyapunov function and introducing an adaptive compensation term for the upper bound of the optimal approximation error and the dead-zone disturbance, the closed-loop control system is proved to be semiglobally uniformly ultimately bounded, with tracking errors converging to zero under the condition that the slopes of unknown dead zones are equal. Simulation results demonstrate the effectiveness of the approach.