Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control
This work addresses computational bottlenecks in model predictive control for real-time applications like autonomous racing, though it appears incremental as it unifies existing methods.
The paper tackled the problem of unifying sequential quadratic programming (SQP) and linear-parameter-varying model predictive control (LPV-MPC) to enhance computational efficiency in robust and stochastic MPC, demonstrating real-time feasibility in autonomous racing experiments.
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.