Stable MPC with maximal terminal sets and quadratic terminal costs

TL;DR

This paper presents a method to compute a quadratic terminal cost for linear MPC that guarantees stability and maximizes the feasible state set, simplifying controller tuning.

Contribution

It introduces a technique to derive a quadratic terminal cost valid over the maximal control invariant set, enhancing stability and feasibility in linear MPC.

Findings

Maximizes the set of feasible states for the controller

Ensures asymptotic stability with standard proofs

Facilitates easy tuning of the controller

Abstract

This paper develops a technique for computing a quadratic terminal cost for linear model predictive controllers that is valid for all states in the maximal control invariant set. This maximizes the set of recursively feasible states for the controller, ensures asymptotic stability using standard proofs, and allows for easy tuning of the controller in linear operation.