Tuning of digital PID controller using particle swarm optimization

PID controllers are one of the most applicable controllers in different industries. The main important need in application of these controllers is their parameters tuning in order to gain desired result. Existing tuning rules for their design are usually based on trial and error which are so time consuming, not accurate and have considerable error. In this paper, an accessible method with high accuracy and speed has been presented for determination of these control parameters, using PSO optimization algorithm and performance assessment criteria. The results show that there is a considerable difference between this method's results and the other method's.

[1]  Daobo Wang,et al.  Novel approach to nonlinear PID parameter optimization using ant colony optimization algorithm , 2006 .

[2]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[3]  Pin Luarn,et al.  A discrete version of particle swarm optimization for flowshop scheduling problems , 2007, Comput. Oper. Res..

[4]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Jun Zhang,et al.  A novel discrete particle swarm optimization to solve traveling salesman problem , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  I. M. Whiting Tools for the implementation of enhanced PID controllers and their use in electro-hydraulic servo applications , 1996 .

[7]  Yi Jiang,et al.  Applying Multi-Swarm Accelerating Particle Swarm Optimization to Dynamic Continuous Functions , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[8]  Qiu-Yu Zhang,et al.  A Hybrid Self-Adaptive Pso Algorithm and its Applications for Partner Selection in Holonic Manufacturing System (HMS) , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  M.B. Abdelhalim,et al.  Constrained and Unconstrained Hardware-Software Partitioning using Particle Swarm Optimization Technique , 2007, IESS.

[11]  Hu Yueming,et al.  Modeling and Analysis of SMT Motion Control System , 2006, 2007 Chinese Control Conference.

[12]  J.G. Vlachogiannis,et al.  A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems , 2006, IEEE Transactions on Power Systems.

[13]  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.

[14]  E. N. Ganesh,et al.  An Experimental Comparative Analysis of Integrated Fuzzy Logic Controller (IFLC) and PID Speed Control of PMDC Micro Motor , 2007 .

[15]  Aviram Margalith,et al.  Optimum Setting for Proportional Controller , 1982, IEEE Transactions on Industrial Electronics.

[16]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[17]  Liu Yang,et al.  Research on Ant Colony Neural Network PID Controller and Application , 2007, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).

[18]  Loi Lei Lai,et al.  GA optimized PID controllers for automatic generation control of two area reheat thermal systems under deregulated environment , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[19]  J. G. Ziegler,et al.  Optimum Settings for Automatic Controllers , 1942, Journal of Fluids Engineering.

[20]  Haozhong Cheng,et al.  New discrete method for particle swarm optimization and its application in transmission network expansion planning , 2007 .

[21]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[22]  Kim-Fung Man,et al.  An optimal fuzzy PID controller , 2001, IEEE Trans. Ind. Electron..

[23]  A. Rubaai,et al.  Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping , 2008, IEEE Transactions on Industry Applications.

[24]  Katsuhiko Ogata,et al.  Discrete-time control systems (2nd ed.) , 1995 .

[25]  Kuanyi Zhu,et al.  Automated postoperative blood pressure control , 2000 .

[26]  Jigui Sun,et al.  An Improved Discrete Particle Swarm Optimization Algorithm for TSP , 2007, 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops.

[27]  M. Tavakoli,et al.  Optimal Tuning of PID Controllers for First Order Plus Time Delay Models Using Dimensional Analysis , 2003, 2003 4th International Conference on Control and Automation Proceedings.

[28]  Hu Yueming,et al.  On PID controllers based on simulated annealing algorithm , 2008, 2008 27th Chinese Control Conference.

[29]  Shinn-Ying Ho,et al.  Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA , 2006, IEEE Transactions on Fuzzy Systems.

[30]  Steve Glickman,et al.  Identification-based PID control tuning for power station processes , 2004, IEEE Transactions on Control Systems Technology.

[31]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[32]  K. Valarmathi,et al.  Particle Swarm Optimization based PI controller tuning for Fermentation Process , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[33]  M. Sami Fadali,et al.  Multivariable PID controller design , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[34]  Pierre-Yves Glorennec,et al.  Tuning fuzzy PID controllers using ant colony optimization , 2009, 2009 17th Mediterranean Conference on Control and Automation.

[35]  Zhongkui Zhu,et al.  A Reinforced Self-Escape Discrete Particle Swarm Optimization for TSP , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[36]  Chuang Fu,et al.  SVC Control System Based on Instantaneous Reactive Power Theory and Fuzzy PID , 2008, IEEE Transactions on Industrial Electronics.

[37]  Chunguang Zhou,et al.  Fuzzy discrete particle swarm optimization for solving traveling salesman problem , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[38]  Ulrich Bodenhofer,et al.  Genetic Algorithms: Theory and Applications , 2002 .

[39]  Qingjin Meng,et al.  The Application of DCS to Waste Heat Power Generation of Cement Factory , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[40]  G. Radhesham,et al.  An Experimental Comparative Analysis of Integrated Fuzzy Logic Controller (IFLC) and PID Speed Control of PMDC Micro Motor , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[41]  Dong Wang,et al.  Research on Control Problem of PenduBot Based on PSO Algorithm , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[42]  Gene A. Tagliarini,et al.  Optimization Using Neural Networks , 1991, IEEE Trans. Computers.

[43]  Alan V. Oppenheim,et al.  Signals & systems (2nd ed.) , 1996 .