4.7 Article

Robust adaptive control for a class of MIMO nonlinear systems with guaranteed error bounds

Journal

IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 48, Issue 5, Pages 728-742

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2003.811250

Keywords

adaptive control; neural networks; nonlinear systems; robustness; switching functions

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The design of stabilizing controllers for multiple-input-multiple-output (MIMO) nonlinear plants with unknown nonlinearities is a challenging problem. The high dimensionality coupled with the inability to identify the nonlinearities online or offline accurately motivates the design of stabilizing controllers based on approximations or on approximate estimates of the plant nonlinearities that are simple enough to be generated in real time. The price paid in such case, could be lack of theoretical guarantees for global stability, and nonzero tracking or regulation error at steady state. In this paper, a nonlinear robust adaptive control algorithm is designed and analyzed for a class of MIMO nonlinear systems with unknown nonlinearities. The proposed control scheme provides a general approach to bypass the stabilizability problem where the estimated plant becomes uncontrollable without any restrictive assumptions. The controller is continuous and guarantees closed-loop semiglobal stability and convergence of the tracking error to a small residual set. The size of the tracking error at steady state can be specified a priori and guaranteed by choosing certain design parameters. A procedure for choosing these parameters is presented. The properties of the proposed control algorithm are demonstrated using simulations.

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