IMC based PID Control Applied to the Benchmark PID18

Abstract In this paper an Internal Model Control (IMC) based proportional-integral-derivative (PID) control is presented and evaluated on the benchmark system presented at the 3 rd IFAC Conference on Advances in Proportional-Integral-Derivative Control (PID 18). The controller is designed based on the model of the benchmark system. Its performance is compared with a computer-aided design tool based on frequency response (FRtool) and against the benchmark reference controller. The results show that the proposed method has a better performance due to the fact that IMC based PID parameters depend totally on the model.

[1]  Ciro Aprea,et al.  Fuzzy control of the compressor speed in a refrigeration plant , 2004 .

[2]  Robin De Keyser,et al.  FRtool: A frequency response tool for CACSD in Matlab® , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[3]  Sigurd Skogestad,et al.  Simple analytic rules for model reduction and PID controller tuning , 2003 .

[4]  Tobias Geyer,et al.  HYBRID MODEL PREDICTIVE CONTROL IN SUPERMARKET REFRIGERATION SYSTEMS , 2005 .

[5]  Jan Dimon Bendtsen,et al.  Robust Aggregator Design for Industrial Thermal Energy Storages in Smart Grid , 2017, IEEE Transactions on Smart Grid.

[6]  Leandro dos Santos Coelho,et al.  Firefly algorithm approach based on chaotic Tinkerbell map applied to multivariable PID controller tuning , 2012, Comput. Math. Appl..

[7]  C. H. Chiou,et al.  The application of fuzzy control on energy saving for multi-unit room air-conditioners , 2009 .

[8]  John Bagterp Jørgensen,et al.  Model predictive control technologies for efficient and flexible power consumption in refrigeration systems , 2012 .

[9]  P. G. Jolly,et al.  Fuzzy Control of Superheat in Container Refrigeration using an Electronic Expansion Valve , 1997 .

[10]  S. Baskar,et al.  Evolutionary algorithms based design of multivariable PID controller , 2009, Expert Syst. Appl..

[11]  Guillermo Bejarano,et al.  Suboptimal hierarchical control strategy to improve energy efficiency of vapour-compression refrigeration systems , 2017 .

[12]  Yanjun Huang,et al.  An energy-saving set-point optimizer with a sliding mode controller for automotive air-conditioning/refrigeration systems , 2017 .

[13]  DOWNLOAD HERE,et al.  Process Control: Modeling, Design and Simulation , 2003 .

[14]  Tore Hägglund,et al.  Advanced PID Control , 2005 .

[15]  Jan Dimon Bendtsen,et al.  MIMO Robust Disturbance Feedback Control for Refrigeration Systems Via an LMI Approach , 2017 .

[16]  Philip Haves,et al.  Model predictive control for the operation of building cooling systems , 2010, Proceedings of the 2010 American Control Conference.

[17]  H. Rasmussen Adaptive superheat control of a refrigeration plant using backstepping , 2008, 2008 International Conference on Control, Automation and Systems.

[18]  J. V. C. Vargasa,et al.  Experimental development of an intelligent refrigeration system , 2005 .

[19]  Guillermo Bejarano,et al.  Multivariable analysis and H∞ control of a one-stage refrigeration cycle , 2015 .

[20]  Sangchul Won,et al.  Super-twisting algorithm-based sliding mode controller for a refrigeration system , 2012, 2012 12th International Conference on Control, Automation and Systems.