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This paper presents a multi-variable Model Predictive Control (MPC) basedcontroller for the one-staged refrigeration cycle model described in thePID2018 Benchmark Challenge. This model represents a two-input, two-outputsystem with strong nonlinearities and high coupling between its variables. Ageneral purpose optimal control problem (OCP) solver Matlab toolbox calledRIOTS is used as the OCP solver for the proposed MPC scheme which allows forstraightforward implementation of the method and for solving a wide range ofconstrained linear and nonlinear optimal control problems. A conditionalintegral (CI) compensator is embedded in the controller to compensate for thesmall steady state errors. This method shows significant improvements inperformance compared to both discrete decentralized control (C1) andmulti-variable PID controller (C2) originally given in PID2018 BenchmarkChallenge as a baseline. Our solution is introduced in detail in this paper andour final results using the overall relative index, $J$, are 0.2 over C1 and0.3 over C2, respectively. In other words, we achieved 80% improvement over C1and 70% improvement over C2. We expect to achieve further improvements whensome optimized searching efforts are used for MPC and CI parameter tuning.

PID2018 Benchmark Challenge: Model Predictive Control With Conditional  Integral Control Using A General Purpose Optimal Control Problem Solver -  RIOTS