Adaptive tracking MPC for nonlinear systems via online linear system identification

TL;DR

This paper introduces an adaptive MPC scheme for unknown nonlinear systems that uses online linear system identification to ensure stability and improve control accuracy, validated through simulations.

Contribution

It proposes a novel adaptive tracking MPC method that combines online linear identification with stability guarantees for nonlinear systems.

Findings

Ensures practical exponential stability of the system.

Outperforms a data-driven MPC scheme in simulations.

Provides a systematic approach for controlling unknown nonlinear dynamics.

Abstract

This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on linear system identification using a moving window of past measurements, and it serves as a local approximation of the underlying nonlinear dynamics. We prove that the presented scheme ensures practical exponential stability of the (unknown) optimal reachable equilibrium for a given output setpoint. Finally, we apply the proposed scheme in simulation and compare it to an alternative direct data-driven MPC scheme based on the Fundamental Lemma.