Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control

TL;DR

This paper unifies SQP and LPV-MPC frameworks using differential formulations, enhancing real-time model predictive control efficiency and demonstrating its effectiveness through simulations and real-world experiments.

Contribution

It introduces a unified framework connecting SQP and LPV-MPC via differential formulations, improving computational efficiency for robust and stochastic MPC.

Findings

Unified SQP and LPV-MPC improve computational efficiency.

Simulation comparisons show similar convergence properties.

Real-time GP-based MPC demonstrates practical feasibility in autonomous racing.

Abstract

This paper presents a unified framework that connects sequential quadratic programming (SQP) and the iterative linear-parameter-varying model predictive control (LPV-MPC) technique. Using the differential formulation of the LPV-MPC, we demonstrate how SQP and LPV-MPC can be unified through a specific choice of scheduling variable and the 2nd Fundamental Theorem of Calculus (FTC) embedding technique and compare their convergence properties. This enables the unification of the zero-order approach of SQP with the LPV-MPC scheduling technique to enhance the computational efficiency of robust and stochastic MPC problems. To demonstrate our findings, we compare the two schemes in a simulation example. Finally, we present real-time feasibility and performance of the zero-order LPV-MPC approach by applying it to Gaussian process (GP)-based MPC for autonomous racing with real-world experiments.