Forward-looking persistent excitation in model predictive control

This work deals with the problem of integrating persistence of excitation into nonlinear constrained model predictive control to estimate uncertain parameters while guaranteeing a stable closed loop. We propose an adaptive tracking model predictive control and conditions which guarantee persistent excitation and uniformly bounded closed loop signals of nonlinear systems, despite bounded noise and parameter uncertainty. This is achieved by actively designing persistence of excitation through the computation of a reference trajectory around some nominal stationary point depending on the parameter estimate, opening up the opportunity of balancing excitation against other requirements. Appealing to the Total Stability Theorem, the results are local and solution evolves in a non-infinitesimal ball in state and estimated parameter.