Adaptive Neural Output Feedback Controller Design With Reduced-Order Observer for a Class of Uncertain Nonlinear SISO Systems

Authors:
Yan-Jun Liu; Shao-Cheng Tong;D. Wang; Tie-Shan Li;C. L.P. Chen
Affiliations:
Sch. of Sci., Liaoning Univ. of Technol., Jinzhou, China;-;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2011

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Abstract

An adaptive output feedback control is studied for uncertain nonlinear single-input-single-output systems with partial unmeasured states. In the scheme, a reduced-order observer (ROO) is designed to estimate those unmeasured states. By employing radial basis function neural networks and incorporating the ROO into a new backstepping design, an adaptive output feedback controller is constructively developed. A prominent advantage is its ability to balance the control action between the state feedback and the output feedback. In addition, the scheme can be still implemented when all the states are not available. The stability of the closed-loop system is guaranteed in the sense that all the signals are semiglobal uniformly ultimately bounded and the system output tracks the reference signal to a bounded compact set. A simulation example is given to validate the effectiveness of the proposed scheme.