Accelerated Model Predictive Control Using Restricted Quadratic Programming⁎This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the Ontario Centres of Excellence (OCE) and Maplesoft. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Maitland, Anson; McPhee, John
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 7001
• Page End: 7006
• Publication Date: 2020
• Subjects: Model-based control -- Predictive control -- Quadratic programming -- Nonlinear programming; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.439
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We present a method to reduce the computational burden of solving a sequence of convex quadratic programs (QPs). By determining offline what search space is most important, we can restrict our online problem to that subspace, reducing the dimension and computational cost of the QP solver. The process we present is very simple requiring surprisingly little data. Further, we present a modified sequential QP algorithm that leverages the restricted QP approach to solve nonlinear programming problems found in model predictive control. Lastly, we apply these to a benchmark MPC problem and demonstrate their effectiveness using a variety of established QP solvers. We demonstrate that QP problems can be solved faster with minimal MPC performance degradation and highlight future directions for this work.