Robust learning control for a class of nonlinear systems

Abstract

A novel nonlinear control scheme, robust learning control (RLC), is developed in this paper. The new RLC system guarantees the global and asymptotic tracking property for various types of dynamic systems with high nonlinearities and uncertainties. In the proposed control scheme, learning control and variable structure control are made to function in a complementary manner. The nonlinear learning control strategy is applied directly to those system uncertainties which can be separated and expressed as products of unknown state-independent functions and known state-dependent functions. For those system uncertainties associated with known bounding functions as the only a priori knowledge, variable structure control strategy is applied to ensure the global asymptotic stability. By virtue of the learning and robust properties, the new control system can easily fulfill control objectives that are impossible for either learning control or variable structure control alone to handle. Based on the Lyapunov's direct method, various important properties concerning learning control such as the needs for resetting condition and derivative signals, whether using iterative control mode or repetitive control mode, are made clear with relation to different control objectives and plant dynamics.