考虑输入饱和的直接自适应神经网络跟踪控制

doi:10.3969/j.issn.0255-8297.2013.03.012

Abstract

Abstract: Based on the Lyapunov stability theory and the backstepping technique, a direct adaptive neural networks controller is proposed for a class of uncertain nonlinear single-input-single-out systems in the presence of input saturation. It is shown that all signals in the closed-loop system are uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Using the dynamic surface control (DSC) technique and neural networks, the problem of explosion of complexity inherent in the conventional backstepping method is avoided. The controller’s singularity problem is removed completely by using a special property of the affine term. A stability analysis subject to the effect of input saturation constrains is conducted with the help of an auxiliary design system. Simulation studies of an application case of a Dalian Maritime University training ship YULONG are given to demonstrate effectiveness and good performance of the proposed scheme.

Key words: adaptive control, neural network, dynamic surface control, input saturation

CLC Number:

TP273

LI Jun-fang, LI Tie-shan. Direct Adaptive Neural Network Tracking Control with Input Saturation[J]. Journal of Applied Sciences, 2013, 31(3): 294-302.

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Direct Adaptive Neural Network Tracking Control with Input Saturation

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