Adaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach

TL;DR

This paper introduces an adaptive learning-based MPC scheme for interconnected systems with uncertain coupling, combining set membership identification and robust control to improve prediction and stability.

Contribution

It develops a novel two-phase online adaptive MPC approach that learns uncertainty sets and adjusts control parameters accordingly for interconnected systems.

Findings

The scheme ensures recursive feasibility and stability.

Simulation results outperform existing robust, adaptive, and learning-based MPC methods.

Abstract

We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty set that contains the coupling strength using online data. In the adaptation phase, rigid tube-based robust MPC is used to compute the optimal predicted states and inputs. Besides computing the optimal trajectories, the MPC ingredients are adapted in the adaptation phase taking the learnt uncertainty set into account. These MPC ingredients include the prestabilizing controller, the rigid tube, the tightened constraints and the terminal ingredients. The recursive feasibility of the proposed scheme as well as the stability of the corresponding closed-loop system are discussed. The developed scheme is compared in simulations to existing schemes including robust, adaptive and learning-based MPC.