Abstract
Recently, an approach of advanced-step nonlinear model predictive control (NMPC) based on nonlinear programming (NLP) sensitivity was developed for controlling large-scale complex plants. It can reduce the online computation time significantly while maintaining the same nominal stability of the conventional NMPC. However, the sensitivity based fast online update method cannot be applied to those cases where the NMPC problem contains a non-convex economic cost function or constraints. In this paper, based on contraction analysis, a novel online update mechanism, differential Lyapunov based MPC, which involves solving a quadratic programming problem with stability constraints is proposed to address this issue. A case study with non-convex state constraints is presented to demonstrate the concepts.
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