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A RBF Network Direct Generalized Predictive Control Based on Tracking Error Adjustment

Yang Zhao, Chi Zhang

Abstract


Generalized predictive control (GPC) is a kind of advance control methods that is of strong robustness, which can overcome the time delay problem effectively and is suitable to unstable non-minimum phase system. But it has the shortcoming of large computation load because of online solving the Diophantine equation, the inverse matrix and the recursion solution by Least squares. So a kind of RBF network direct generalized predictive control is presented to overcome the shortcoming of large computation load of original GPC. In the method, a class of nonlinear system is replaced by a time varying linear system based on the mean value theorem. Then RBF network is used to design predictive controller directly. Finally the controller parameter θ¬u is adjusted adaptively based on tracking error. It is proved that the proposed method can make the estimated generalized error converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the method. The method can be used in the predictive control system in real-time manner.

Keywords


nonlinear system, GPC, RBF network, stability analysis.

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