Constraint-adaptive MPC for large-scale systems: Satisfying state constraints without imposing them

TL;DR

This paper introduces constraint-adaptive MPC schemes that dynamically select constraints to reduce computational complexity in large-scale nonlinear systems while ensuring recursive feasibility and constraint satisfaction.

Contribution

The paper presents novel MPC schemes that adaptively choose constraints online, significantly reducing computation time without compromising control performance.

Findings

Computation time reduced by over two orders of magnitude.

Maintains recursive feasibility and constraint satisfaction.

Effective for large-scale nonlinear systems.

Abstract

Model Predictive Control (MPC) is a successful control methodology, which is applied to increasingly complex systems. However, real-time feasibility of MPC can be challenging for complex systems, certainly when an (extremely) large number of constraints have to be adhered to. For such scenarios with a large number of state constraints, this paper proposes two novel MPC schemes for general nonlinear systems, which we call constraint-adaptive MPC. These novel schemes dynamically select at each time step a (varying) set of constraints that are included in the on-line optimization problem. Carefully selecting the included constraints can significantly reduce, as we will demonstrate, the computational complexity with often only a slight impact on the closed-loop performance. Although not all (state) constraints are imposed in the on-line optimization, the schemes still guarantee recursive feasibility and constraint satisfaction. A numerical case study illustrates the proposed MPC schemes and demonstrates the achieved computation time improvements exceeding two orders of magnitude without loss of performance.