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An Improved Particle Swarm Optimization (IPSO) Approach for Identification and Control of Stable and Unstable Systems

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In this paper, an Improved Particle Swarm Optimization (IPSO) technique is generalized to identify and control four systems of different types of behaviors. This was possible thanks to the use of a new initialization strategy of partitioning of particles, which helps PSO to converge faster to the correct region in the research space. The choice of an enhanced fitness function consisting of the weighted sum of the objectives gives better performances compared to those found using four other commonly used performance indices (ISE, IAE, ITAE, and ITSE). The validity of the model chosen for identifying these four types of behaviors is proved, and the control of these systems using IPSO and many conventional optimization methods such as Ziegler-Nichols, Graham-Lathrop, and Reference Model has been compared and confirmed that IPSO generates a high-quality solution with a short calculation time and a stable convergence feature. Moreover, results confirmed that the IPSO optimized PID is the best as it has good performance and good robustness and it is insensitive to perturbations.
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PID; Ziegler-Nichols; Graham-Lathrop; Reference Model; Improved PSO; Identification; Stabilization

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Astrom, K. J., PID controllers: theory, design and tuning. Instrument society of America 1995.

Kasilingam G., Pasupuleti J., Coordination of PSS and PID controller for power system stability enhancement–overview. Indian Journal of Science and Technology, Vol. 8(12). p. 1-10. 2015.

Deif, T., Kassem, A., El Baioumi, G., Modeling and Attitude Stabilization of Indoor Quad Rotor, (2014) International Review of Aerospace Engineering (IREASE), 7 (2), pp. 43-47.

Aghajani, S., Joneidi, I., Kalantar, M., Mortezapour, V., Modeling and Simulation of a PV/FC/UC Hybrid Energy System for Stand Alone Applications, (2014) International Journal on Energy Conversion (IRECON), 2 (1), pp. 26-34.

Ho, M. T., Datta, A., Bhattacharyya, S. P., A linear programming characterization of all stabilizing PID controllers, Proceedings of the. IEEE American Control Conference. p. 3922-3928. 1997.

Graham, D., Lathrop, R. C., The synthesis of optimum transient response: Criteria and standard forms, Transactions AIEE, II: Vol. 72 No. 5, p. 273-288. 1953.

Naslin, P. Essentials of optimal control. Iliffe & Sons Ltd, London. 1968.

Payam, A., Hassani, F., Fathipour, M., Design of Hybrid Closed Loop Control Systems for a MEMS Accelerometer Using Nonlinear Control Principles, (2014) International Review of Aerospace Engineering (IREASE), 7 (5), pp. 164-170.

Bouallegue, S., Khoud, K., Integral Backstepping Control Prototyping for a Quad Tilt Wing Unmanned Aerial Vehicle, (2016) International Review of Aerospace Engineering (IREASE), 9 (5), pp. 152-161.

Sobhani, B., Toulabi, M., Ebrahimian, H., Two Novel Proposed Controllers for a Wind Energy Conversion System, (2015) International Journal on Energy Conversion (IRECON), 3 (2), pp. 68-75.

Zhao, D. S., Neural Network Based PID Control for Quadrotor Aircraft, International Conference on Intelligent Science and Big Data Engineering, Springer International Publishing, p. 287-297, 2015.

Goldberg, D.E. Genetic Algorithms in search, Optimization and Machine Learning.Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA.1989.

Mjahed, M. Optimization of classification tasks by using genetic algorithms. Proceedings of INFOS, the 7th IEEE International Conference on Informatics and Systems, IEEEXplore, p.1-4. 2010.

Mjahed, M. PID Controller Design using Genetic Algorithm Technique, Proceedings of ICICR. Istanbul, Turkey. p.31-34. 2014.

Dorigo, M., Maniezzo, V., Colorni, A. Ant system: optimization by a colony of cooperating agents, Systems, IEEE Transactions on Man, and Cybernetics. Part B: Cybernetics, 26 (1), p. 29-41. 1996.

Kennedy, J., Eberhart, R. Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, p. 1942-1948. 1995.

Damiano, A., Gatto, G., Marongiu, I., Meo, S., Perfetto, A., Serpi, A. Single-stage grid connected PV inverter with active and reactive power flow control via PSO-PR based current controlled SVPWM, (2012) International Review of Electrical Engineering, 7 (4), pp. 4647-4654.

Jeraldin, A.D. Adaptive Particle Swarm Optimization based system identification and internal model sliding mode controller for mass flow system, Journal of Control Engineering and Applied Informatics, 17 (4), p. 3-13. 2015.

Meo, S., Zohoori, A., Vahedi, A., Optimal design of permanent magnet flux switching generator for wind applications via artificial neural network and multi-objective particle swarm optimization hybrid approach, (2016) Energy Conversion and Management, 110, pp. 230-239.

Dadgar, M., Jafari, S., Hamzeh, A. A PSO-based multi-robot cooperation method for target searching in unknown environments, Neurocomputing, Vol. 177, p.62-74. 2016.

Sabir, M. , Ali, T. Optimal PID controller design through swarm intelligence algorithms for sun tracking system. Applied Mathematics and Computation, Vol. 274, p. 690-699. 2016.

Melo, H., Watada, J. Gaussian-PSO with fuzzy reasoning based on structural learning for training a Neural Network. Neurocomputing, Vol. 172, p. 405-412. 2016.

Astrom, K.J., Hagglund, T. Revisiting the Ziegler-Nichols step response method for PID control. Journal of process control, 14, p. 635-650. 2004.

Mjahed, M. Linear control systems, Lectures notes, Royal School of Aeronautics, Marrakech. 2014.

Evers, G. I. An automatic regrouping mechanism to deal with stagnation in particle swarm optimization. PhD thesis,University of Texas-Pan American. 2009.

Marini, F., Walczak, B. Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, Vol. 149, p. 153-165. 2015.

Wong, C. C., Li S. A., Wang H. Y. Optimal PID controller design for AVR system. Tamkang Journal of Science and Engineering, Vol. 12, No. 3, p. 259-270. 2009.

Johansen, T. A., Foss, B. A. Multiple model approaches to modelling and control. International Journal of control, Vol. 72, No. 7-8, p. 575-575. 1999.

Solihin, M. I., Tack, L. F., Kean, M. L. Tuning of PID controller using particle swarm optimization (PSO). International Journal on Advanced Science, Engineering and Information Technology, Vol. 1, No. 4, p. 458-461. 2011.

Latha, K., Rajinikanth, V., Surekha, P. M. PSO-based PID controller design for a class of stable and unstable systems, ISRN Artificial Intelligence, Vol. 2013. p.1-11. 2013.


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