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Nonlinear systems under uncertainty are difficult to regulate with guaranteed stability and optimality. This study presents a switching control strategy, which consists of robust control Lyapunov function-based predictive controller and approximate. dynamic programming-based controller. The former guarantees the robust stability within a level set, referred to as region of attraction (ROA). The latter...
In order to improve the performance of nonlinear model predictive control (NMPC) in the presence of disturbances or model uncertainties, an approximate dynamic programming (ADP) control scheme is proposed. Namely, the Bellman's optimality principle is employed to determine the input based on the approximate value function constructed from the historical operation data. In addition, the support vector...
A major difficulty associated with using an empirical nonlinear model for model-based control is that the model can be unduly extrapolated into regions of the state space where identification data were scarce or even nonexistent. Optimal control solutions obtained with such over-extrapolations can result in performances far worse than predicted by the model. In the multi-step predictive control setting,...
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