Call for Paper - March 2022 Edition
IJCA solicits original research papers for the March 2022 Edition. Last date of manuscript submission is February 22, 2022. Read More

Tuning of a PID Controller for a Real Time Industrial Process using Particle Swarm Optimization

Print
PDF
Evolutionary Computation for Optimization Techniques
© 2010 by IJCA Journal
Number 1 - Article 7
Year of Publication: 2010
Authors:
Dr.S.M.Girirajkumar
Atal.A.Kumar
Dr.N.Anantharaman
10.5120/1528-131

Dr.S.M.Girirajkumar, Atal.A.Kumar and Dr.N.Anantharaman. Tuning of a PID Controller for a Real Time Industrial Process using Particle Swarm Optimization. IJCA Special Issue on Evolutionary Computation (1):35–40, 2010. Full text available. BibTeX

@article{key:article,
	author = {Dr.S.M.Girirajkumar and Atal.A.Kumar and Dr.N.Anantharaman},
	title = {Tuning of a PID Controller for a Real Time Industrial Process using Particle Swarm Optimization},
	journal = {IJCA Special Issue on Evolutionary Computation},
	year = {2010},
	number = {1},
	pages = {35--40},
	note = {Full text available}
}

Abstract

Measurement of level, temperature, pressure and flow parameters are very vital in all process industries. The model for such a real time process is identified and validated .Real time industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable. Determination or tuning of the PID parameters continues to be important as these parameters have a great influence on the stability and performance of the control system. most of the processes are complex and nonlinear in nature resulting into their poor performance when controlled by traditional tuned PID controllers. The need for improved performance of the process has led to the development of optimal controllers. So the control engineers are on look for automatic tuning procedures. This paper discusses in detail about the Particle swarm Optimization (PSO) algorithm, an Evolutionary Computation (EC) technique, and its implementation in PID tuning for an industrial process . Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the proposed method in terms of time domain specification and performance indices.

Reference

  • Asriel U. Levin and Kumpati S. Narendra, Control of nonlinear dynamical systems using Neural Networks- Part II : observability, identification and control, IEEE Transactions on Neural Networks, Vol. 7, No. 1, January 1996.
  • Åström. K. J and Hägglund. T, PID controllers: theory, design, and tuning. Instrument Society of America, ISA, 1995.
  • Baumgartner U, Magele Ch, Renhart W. Pareto optimality and particle swarm optimization. IEEE Trans Magn;40(2):1172–5,2004.
  • Bisowarno. B. H, Tian. Y. C, and Tade .M. O, “Model gain scheduling control of an ethyl tert-butyl ether reactive distillation column,” Ind. Eng. Chem. Res., vol. 42, pp. 3584-3391, 2003.
  • Castro LN, Timmis JI. Artificial immune systems: a new computationalintelligence approach. London, UK: Springer-Verlag; 2002.
  • CoelloC, LunaE. Use of particle swarm optimization to design Combinational logic circuits. In:TyrellA, HaddowP, TorresenJ, editors. 5th International conference on evolvable systems: from biology to hardware,ICES2003.Lecture notes in computer science, vol.2606. Trondheim, Norway :Springer ;p.398–409,2003.
  • Coelho.L.D.S, Sierakowski. C. A,” A software tool for teaching of particle swarm optimization fundamentals “, Advances in Engineering Software 39 (2008) 877–887
  • Dorigo M, Di Caro G. The ant colony optimization meta-heuristic. In:Corne D, Dorigo M, Glover F, editors. New ideas in optimization. McGraw-Hill; p. 11–32, 1999.
  • Eberhart. R. C and Kennedy. J. F, “A new optimizer using particle swarm theory,” Proceedings of International Symposium on Micro Machine and Human Science, Japan, pp. 39-43, 1995.
  • Fourie PC, Groenwold AA. The particle swarm optimization algorithm in size and shape optimization. Structural Multidisciplinary Optimization;23(4):259–67,2002.
  • Javed Alam Jan, Bohumil Sulc, Evolutionary computing methods for optimizing virtual reality process models, International Carpathian control conference ICCC’2002, Malenovice, Czech Republic, May 27-30, 2002.
  • Kannan S, Slochanal SMR, Subbaraj P, Padhy NP. Application of particle swarm optimization technique and its variant to generation expansion planning problem. Electr Power Sys Res;70(3): 203–10,2004.
  • Karray .F, Gueaieb. W, and Al-Sharhan .S, “The hierarchical expert tuning of pid controllers using tools of soft computing,” IEEE Transactions on Systems, Man, and Cybernetics  Part B: Cybernetics, vol. 32, no. 1, pp. 77-90, 2002.
  • Kennedy JF, Eberhart RC. Particle swarm optimization. In: Proceedingsof the IEEE international conference on neural networks, vol. 4. Perth, Australia; p. 1942–48,1995.
  • Kennedy JF, Eberhart RC, Shi Y. Swarm intelligence. San Francisco: Morgan Kaufman; 2001.
  • Krohling RA, Coelho LS. Coevolutionary particle swarm optimization using gaussian distribution for solving constrained optimization problems. IEEE Trans Syst, Man and Cybern, Part B: Cybern ;36(6):1407–16,2006.
  • Krohling R, CoelhoL, ShiY. Cooperative particle swarm optimization for robust control system design.In:7th Online world conference on soft computing in industrial applications,2002.
  • Mehrdad Salami and Greg Cain, An adaptive PID controller based on Genetic algorithm processor, Genetic algorithms in engineering systems: Innovations and applications, 1214, September 1995, Conference publication No. 414, IEE 1995.
  • Muller SD, Marchetto J, Airaghi S, Koumoutsakos P. Optimization based on bacterial chemotaxis. IEEE Trans Evolut Comput ;pp6–29,2002.
  • Oliveira,P ,CunhaJ, CoelhoJ .Design of pid controllers using the Particle swarm algorithm. In: Twenty-first IASTED international Conference :modelling, identification, and control(MIC2002),Innsbruck,Austria,2002.
  • Parsopoulos KE, Vrahatis MV. On the computation of all minimizers through particle swarm optimization. IEEE Trans Evolutionary Computaion;8(3):211–24,2004.
  • Robinson J, Samii YR. Particle swarm optimization in electromagnetic IEEE Trans Antenn Propag ;52(2):397–407,2004.
  • Simon Fabri and Visakan Kadirkamanathan, Dynamic structure neural networks for stable adaptive control of nonlinear systems, IEEE Transactions on Neural Networks, Vol. 7, No. 5, September1996.
  • Su Whan Sung, In-Beum Lee and Jitae Lee, Modified Proportional-Integral Derivative (PID) Controller and a New Tuning Method for the PID Controller, Ind. Eng. Chem. Res., 34, pp. 4127-4132, 1995.
  • Wah. B , Chen. Y, Constrained genetic algorithms and their applications in nonlinear constrained optimization, In Proceedings of International conference on tools with artificial intelligence, IEEE, November 2000, pp. 286-293
  • Yu XM, Xiong XY, Wu YW. A PSO-based approach to optimal capacitor placement with harmonic distortion consideration. Electr Power Sys Res ;vol71(1).pp:27–33, 2004.
  • Zhang L, Yu H, Hu S. A new approach to improve particle swarm optimization, GECCO 2003. In: Cantu´-Paz E. et al., editor. LNCS 2723. Springer, London, UK; p. 1341–39, 2003.
  • Zhang W, Liu,M. ClercY. An adaptive pso algorithm for reactive Power optimization .In: Sixth international conference on advances inPowersystem control, operation and management (APSCOM) 2003,HongKong,China,p.302–7,2003.
  • Zheng Y,MaL,ZhangL,QianJ.Robust pid controller design using Particle swarm optimizer.In: IEEE international symposium on Intelligence control,p.974,2003.