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Nonlinear multivariable adaptive control using multiple models and neural networks. (English) Zbl 1282.93145
Summary: In this paper, a multivariable adaptive control approach is proposed for a class of unknown nonlinear multivariable discrete-time dynamical systems. By introducing a \(k\)-difference operator, the nonlinear terms of the system are not required to be globally bounded. The proposed adaptive control scheme is composed of a linear adaptive controller, a neural-network-based nonlinear adaptive controller and a switching mechanism. The linear controller can assure boundedness of the input and output signals, and the neural network nonlinear controller can improve performance of the system. By using the switching scheme between the linear and nonlinear controllers, it is demonstrated that improved performance and stability can be achieved simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.

93C40 Adaptive control/observation systems
93C35 Multivariable systems, multidimensional control systems
93C10 Nonlinear systems in control theory
93C55 Discrete-time control/observation systems
Full Text: DOI
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