Constraint Removal for MPC with Performance Preservation and a Hyperthermia Cancer Treatment Case Study

TL;DR

This paper introduces a constraint-adaptive MPC framework that reduces computational complexity by selecting a subset of constraints at each step, maintaining performance and feasibility, demonstrated on hyperthermia cancer treatment with significant speedup.

Contribution

The paper presents a novel constraint-adaptive MPC method that preserves performance while significantly reducing computation time using reachable set computations.

Findings

Two-orders of magnitude reduction in computation time.

Maintains identical closed-loop performance as original MPC.

Ensures recursive feasibility and constraint satisfaction.

Abstract

Model predictive control (MPC) is an optimization-based control strategy with broad industrial adoption. Unfortunately, the required computation time to solve the receding-horizon MPC optimization problem can become prohibitively large for many applications with a large number of state constraints. This large number of state constraints can, for instance, originate from spatially discretizing a partial differential equation of which the solution has to satisfy constraints over the full spatial domain. This is particularly the case in MPC for RF-based hyperthermia cancer treatments, which forms a strong motivation for this study. To address this problem, we propose a novel constraint-adaptive MPC framework for linear discrete-time systems. In this framework, we select at each time-step a subset of the state constraints that are included in the optimization problem, thereby reducing the online computational burden. Critically, our framework guarantees the same closed-loop performance, recursive feasibility, and constraint satisfaction properties as the original (non-reduced) MPC scheme. We achieve this result by efficiently exploiting reachable set computations and the MPC cost function. We will demonstrate our novel method using a hyperthermia cancer treatment case study showing a two-orders of magnitude improvement in computation time, with identical closed-loop performance as the original (non-reduced) MPC scheme.