Abstract
This paper describes a receding horizon control strategy for constrained nonlinear systems which uses adaptive techniques to minimize continuously an infinite horizon performance cost subject to constraints. Predictions of future plant behaviour evolve as the outputs of an autonomous nonlinear system. An upper bound, obtained via a passivity assumption, on the standard quadratic cost is used in conjunction with a characterization of constraints in terms of an admissible set for the prediction system state to derive an asymptotically optimal control law. Closed-loop stability and convergence are guaranteed without the need for end-point constraints.
Published Version
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