Robust adaptive learning control for nonlinear systems with extended matching unstructured uncertainties

摘要

The exponential output tracking problem for a class of single-input, single-output uncertain nonlinear systems, including systems with extended matching unstructured uncertainties and without a well-defined global relative degree, is addressed. Conditions on the uncertain system dynamics are derived, which allow us to design a state-feedback learning control achieving semi-global exponential output tracking of sufficiently smooth and periodic reference signals of known period, while guaranteeing l _2 and l _∞ transient performances during the learning phase. The application of the proposed learning approach to the position tracking control problem for uncertain permanent magnet step motors with non-sinusoidal flux distribution and uncertain position-dependent load torque allows us to provide a solution to a yet unsolved problem.