Linear tracking MPC for nonlinear systems Part I: The model-based case

TL;DR

This paper introduces a linearized model predictive control scheme for nonlinear systems that guarantees exponential stability and offers computational efficiency comparable to nonlinear MPC, demonstrated on a chemical reactor example.

Contribution

It proposes a novel linearization-based MPC approach with stability guarantees and practical efficiency for nonlinear systems, extending to data-driven models in future work.

Findings

Exponential stability of the proposed MPC scheme.

Comparable performance to nonlinear MPC in a reactor example.

Significant computational efficiency gains.

Abstract

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