PID2018 Benchmark Challenge: learning feedforward control

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller.

[1]  Christian J.L. Hermes,et al.  Assessment of the controlling envelope of a model-based multivariable controller for vapor compression refrigeration systems , 2010 .

[2]  Christian J.L. Hermes,et al.  A model-driven multivariable controller for vapor compression refrigeration systems , 2009 .

[3]  N. Lawrence Ricker,et al.  Predictive hybrid control of the supermarket refrigeration benchmark process , 2010 .

[4]  Jiangjiang Wang,et al.  Study of Neural Network PID Control in Variable-frequency Air-conditioning System , 2007, 2007 IEEE International Conference on Control and Automation.

[5]  F. Méndez,et al.  PID control for a single-stage transcritical CO2 refrigeration cycle , 2014 .

[6]  Theodorus J.A. de Vries,et al.  Linear motor motion control using a learning feedworward controller , 1997 .

[7]  Lennart Blanken,et al.  Optimal estimation of rational feedforward controllers: An instrumental variable approach , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[8]  Fernando Morilla,et al.  Benchmark for PID control of Refrigeration Systems based on Vapour Compression , 2018 .

[9]  J. van Amerongen,et al.  Learning feedforward controller for a mobile robot vehicle , 1996 .

[10]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[11]  J. R. Holm,et al.  Modelling and Multi-Variable Control of Refrigeration Systems , 2003 .

[12]  W.J.R. Velthuis,et al.  Learning feedforward control of a flexible beam , 1996, Proceedings of the 1996 IEEE International Symposium on Intelligent Control.

[13]  Michael J. Grimble,et al.  Iterative Learning Control for Deterministic Systems , 1992 .

[14]  Theodorus J.A. de Vries,et al.  Regularisation in Learning Feed-Forward Control , 2000 .

[15]  W.J.R. Velthuis,et al.  Experimental verification of the stability analysis of learning feed-forward control , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[16]  Xiang-Dong He,et al.  Dynamic modeling and multivariable control of vapor compression cycles in air conditioning systems , 1996 .

[17]  César de Prada,et al.  Hybrid NMPC of supermarket display cases , 2009 .

[18]  Jackson Braz Marcinichen,et al.  A DUAL SISO CONTROLLER FOR A VAPOR COMPRESSION REFRIGERATION SYSTEM , 2008 .

[19]  Wenjian Cai,et al.  Normalized decoupling control for high-dimensional MIMO processes for application in room temperature control HVAC systems , 2010 .

[20]  Wubbe Jan Roelf Velthuis,et al.  Learning feed-forward control - theory, design and applications - , 2000 .

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

[22]  C. Changenet,et al.  Predictive functional control of an expansion valve for minimizing the superheat of an evaporator , 2010 .

[23]  M. Farrokhi,et al.  Neuro-Predictive Control for Automotive Air Conditioning System , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[24]  Jiabin Wang,et al.  A Learning Feed-Forward Current Controller for Linear Reciprocating Vapor Compressors , 2011, IEEE Transactions on Industrial Electronics.

[25]  Masoomeh Taherkhani,et al.  LFFC schems base on B-spline network with static output feedback controller for UPS inverters , 2011, 2011 2nd Power Electronics, Drive Systems and Technologies Conference.

[26]  Yangquan Chen,et al.  PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm , 2018, ArXiv.

[27]  Kevin L. Moore,et al.  Learning feedforward control using a Dilated B-spline network: frequency Domain Analysis and design , 2004, IEEE Transactions on Neural Networks.

[28]  C. Underwood,et al.  ANALYSING MULTIVARIABLE CONTROL OF REFRIGERATION PLANT USING MATLAB/SIMULINK , 2001 .