Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
This work addresses control design for systems with sparse impulse responses, offering a method to handle constraints and improve performance, but it is incremental as it builds on existing MPC and compressed sensing techniques.
The paper tackles adaptive stochastic model predictive control for linear systems with unknown sparse parameters by estimating them with recursive least squares and refining with basis pursuit denoising, reformulating constraints via distributionally robust optimization into convex problems, and demonstrates performance gains over a baseline in numerical examples.
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.