A globally stabilizing nonlinear model predictive control framework

A globally stabilizing nonlinear predictive control (NMPC) framework is developed by a simple design of the terminal cost used in relevant optimization setup.We first adapt the recent results on the finite horizon state-dependent Riccati equation (SDRE) based control for unconstrained nonlinear systems to prove that such a control can be globally stabilizing in case a sufficiently large optimization horizon is selected. Then, we use the resulting globally stabilizing Lyapunov function as a terminal cost within the optimization setup to get a novel NMPC. We also adapt the results on the stability of the NMPC, based on the control Lyapunov function approach to prove global stability of the proposed control framework. The results are validated through extensive simulation setups for some unconstrained nonlinear dynamical systems.

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