PID2018 Benchmark Challenge: Model Predictive Control With Conditional Integral Control Using A General Purpose Optimal Control Problem Solver – RIOTS. Issue 4 (2018)

• Record Type: Journal Article
• Authors: Dehghan, Sina; Zhao, Tiebiao; Zhao, Yang; Yuan, Jie; Ates, Abdullah; Chen, YangQuan
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 4(2018)
• Page Start: 882
• Page End: 887
• Publication Date: 2018
• Subjects: Model predictive Control -- RIOTS -- Optimal Control Problems Solver -- PID2018 Benchmark Challenge -- performance improvement; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.06.112
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper presents a multi-variable Model Predictive Control (MPC) based controller for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge. This model represents a two-input, two-output system with strong nonlinearities and high coupling between its variables. A general purpose optimal control problem (OCP) solver Matlab toolbox called RIOTS is used as the OCP solver for the proposed MPC scheme which allows for straightforward implementation of the method and for solving a wide range of constrained linear and nonlinear optimal control problems. A conditional integral (CI) compensator is embedded in the controller to compensate for the small steady state errors. This method shows significant improvements in performance compared to both discrete decentralized control (C1) and multi-variable PID controller (C2) originally given in PID2018 Benchmark Challenge as a baseline. Our solution is introduced in detail in this paper and our final results using the overall relative index, J, are 0.2 over C1 and 0.3 over C2, respectively. In other words, we achieved 80% improvement over C1 and 70% improvement over C2. We expect to achieve further improvements when some optimized searching efforts are used for MPC and CI parameter tuning.