An Accelerated Proximal Gradient-based Model Predictive Control Algorithm

2021/09/09 by Jia Wang, Ying Yang, Wang, Jia +1
Chemistry · Computer Science · #FOS: Electrical engineering #FOS: Mathematics #Metal-Organic Frameworks: Synthesis and Applications #Optimization and Control (math.OC) #Optimization and Search Problems #Stochastic Gradient Optimization Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2109.04405

openalex publication_date 2021/09/09 · openalex created_date 2022/07/17 · openalex updated_date 2026/07/28

Abstract

In this letter, an accelerated quadratic programming (QP) algorithm is proposed based on the proximal gradient method. The algorithm can achieve convergence rate O(1/p^α), where p is the iteration number and α is the given positive integer. The proposed algorithm improves the convergence rate of existing algorithms that achieve O(1/p²). The key idea is that iterative parameters are selected from a group of specific high order polynomial equations. The performance of the proposed algorithm is assessed on the randomly generated model predictive control (MPC) optimization problems. The experimental results show that our algorithm can outperform the state-of-the-art optimization software MOSEK and ECOS for the small size MPC problems.

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