Nonlinear output-feedback model predictive control with moving horizon estimation

We introduce an output-feedback approach to model predictive control that combines state estimation and control into a single min-max optimization. Under appropriate assumptions that ensure controllability and observability of the nonlinear process to be controlled, we prove that the state of the system remains bounded and establish bounds on the tracking error for trajectory tracking problems. The results apply both to infinite and finite-horizon optimizations, the latter requiring reversible dynamics and the use of a terminal cost that is an ISS-control Lyapunov function with respect to a disturbance input. A numerical example is presented that illustrates these results.