Linear Tracking MPC for Nonlinear Systems—Part I: The Model-Based Case

In this article, we develop a tracking model predictive control (MPC) scheme for nonlinear systems using the linearized dynamics at current state as prediction model. Under reasonable assumptions on dynamics, prove that proposed MPC exponentially stabilizes optimal reachable equilibrium w.r.t. desired target setpoint. Our theoretical results rely fact that, close to steady-state manifold, error of linearization is small, and hence, can slide along manifold toward equilibrium. The closed-loop stability properties mainly depend cost matrix, which allows us trade off performance, robustness, size region attraction. an application continuous stirred tank reactor, show scheme, only requires solving convex quadratic program online, has comparable performance while being computationally significantly more efficient. Furthermore, our provide basis controlling based data-dependent linear models, explore in companion paper.