Navigation College, Dalian Maritime University, Dalian, Liaoning, 116026, China

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Abstract—This note makes effort at the problem of robust adaptive control for a class of nonlinear MIMO systems with unknown time-delays. A concise adaptive neural control scheme is developed by using the Backstepping method, the Lyapunov-Krasovskii functional and a novel “MLN” technique. Unlike the existing literatures, the actual control laws are only composed of the state variables, the reference signals and their derivatives, independent of the designed virtual controls and the other intermediate variables. In addition, only n (the number of system outputs) neural networks are introduced to compensate the nonlinear uncertainties of the whole system. Thus, the outstanding advantage of the proposed scheme is that the control law with a concise structure is model-independent and easy to implement in practical engineering, due to less computational burden. The corresponding scheme guarantees uniform ultimate boundedness of all the signals in the closed-loop system, and the tracking errors can converge to a arbitrary small neighborhood of zero. Finally, a simulation example illustrates the effectiveness of the proposed scheme.

Index Terms—robust adaptive control, neural networks, MIMO systems, time delays

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Cite: Guoqing Zhang, Xianku Zhang, Guoping Xu, "A Novel Concise Adaptive Neural Control for a Class of Nonlinear MIMO Systems with Unknown Time Delays," Journal of Computers vol. 9, no. 1, pp. 65-71, 2014.