Work

Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection

Year: 2025

Type: conference-paper

Abstract: Configuration-Constrained Tube Model Predictive Control (CCTMPC) offers flexibility through polytopic parameterization of invariant sets and optimization of an associated vertex control law. However, this flexibility introduces a tradeoff between set accuracy and computational complexity. This paper addresses it with two contributions. First, a structured framework is proposed that restricts optimization degrees of freedom in a systematic way, significantly reducing online computation while retaining stability guarantees. This framework aligns with Homothetic Tube MPC (HTMPC) under maximal constraints. Second, a template refinement algorithm is introduced, which iteratively solves quadratic programs to balance polytope complexity and conservatism. Simulations on an illustrative benchmark and a high-dimensional tenstate system demonstrate the contributions’ efficiency, achieving robust performance with minimal computational overhead. The results validate a practical pathway to exploit CCTMPC’s adaptability without compromising real-time viability.

Source: Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes

DOI: 10.1109/cdc57313.2025.11312697

Authors: Filippo Badalamenti, Sampath Kumar Mulagaleti, Mario Eduardo Villanueva, Boris Houska, Alberto Bemporad

Institutions: IMT School for Advanced Studies Lucca, ShanghaiTech University

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Research Council, HORIZON EUROPE European Research Council

Award: Computational model predictive and adaptive control tools