Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint

TL;DR

This paper introduces a novel adaptive stochastic MPC approach for linear systems with sparse FIR models, leveraging compressed sensing techniques to improve control performance under constraints.

Contribution

It develops a convex optimization-based MPC method that exploits system sparsity via basis pursuit denoising, enhancing control accuracy and robustness.

Findings

Demonstrates improved control performance over non-sparsity-aware methods

Handles hard input and probabilistic output constraints effectively

Uses distributionally robust optimization for tractable probabilistic constraints

Abstract

This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances. We consider multi-input, multi-output systems that can be expressed by a Finite Impulse Response (FIR) model. The parameters of the FIR model corresponding to each output are unknown but assumed sparse. We estimate these parameters using the Recursive Least Squares algorithm. The estimates are then improved using set-based bounds obtained by solving the Basis Pursuit Denoising [1] problem. Our approach is able to handle hard input constraints and probabilistic output constraints. Using tools from distributionally robust optimization, we reformulate the probabilistic output constraints as tractable convex second-order cone constraints, which enables us to pose our MPC design task as a convex optimization problem. The efficacy of the developed algorithm is highlighted with a thorough numerical example, where we demonstrate performance gain over the counterpart algorithm of [2], which does not utilize the sparsity information of the system impulse response parameters during control design.