Pd-Type on-Line Learning Control for Systems with State uncertainties and Measurement disturbances

In this paper, a PD-type on-line learning control (OLC) is proposed for the tracking problem in a class of nonlinear time-varying systems with state uncertainties and measurement disturbances. In this proposed OLC algorithm, we use a combination of PD feedback control and feedforward control that is based on the previous control input profiles in an iterative manner. The proposed control scheme has a simple and straightforward structure that leads to its easy implementation. The uniform boundary of the tracking error is established based on the convergence analysis. It is demonstrated that the proposed control algorithm is robust in coping with the state uncertainties and measurement disturbances that result from the system modeling inaccuracies and the initial state errors. Further, one unique feature of the proposed PD-type on-line learning control is that the controller allows a wide range of control gains to be selected to improve its convergence rate. Simulation studies demonstrate the effectiveness of the proposed control algorithm.

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