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CLC number: TP273
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Received: 2007-11-25
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A closed-loop particle swarm optimizer for multivariable process controller design
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Kai HAN, Jun ZHAO, Zu-hua XU, Ji-xin QIAN. A closed-loop particle swarm optimizer for multivariable process controller design[J]. Journal of Zhejiang University Science A, 2008, 9(8): 1050-1060.
@article{title="A closed-loop particle swarm optimizer for multivariable process controller design",
author="Kai HAN, Jun ZHAO, Zu-hua XU, Ji-xin QIAN",
journal="Journal of Zhejiang University Science A",
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pages="1050-1060",
year="2008",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0720081"
}
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%A Kai HAN
%A Jun ZHAO
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%A Ji-xin QIAN
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720081
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T1 - A closed-loop particle swarm optimizer for multivariable process controller design
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A1 - Jun ZHAO
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A1 - Ji-xin QIAN
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 8
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0720081
Abstract: Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.
[1] Angeline, P.J., 1998. Evolutionary Optimization versus Particle Swarm Optimization and Philosophy and Performance Difference. Proc. 7th Annual Conf. on Evolutionary Programming, San Diego, USA, p.601-610.
[2] Campo, P., Morari, M., 1994. Achievable closed-loop properties of systems under decentralized control: conditions involving the steady-state gain. IEEE Trans. on Automatic Control, 39(5):932-943.
[3] Chang, W.D., 2007. A multi-crossover genetic approach to multivariable PID controllers tuning. Expert Syst. Appl., 33(3):620-626.
[4] Chatterjee, A., Siarry, P., 2006. Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Comput. Oper. Res., 33(3):859-871.
[5] Cutler, C.R., 1983. Dynamic Matrix Control—An Optimal Multivariable Control Algorithm with Constraints. Ph.D Thesis, University of Houston, Houston, TX.
[6] Eberhart, R.C., Kennedy, J., 1995a. Particle Swarm Optimization. Proc. IEEE Int. Conf. on Neural Networks, Perth, Australia, p.1942-1948.
[7] Eberhart, R.C., Kennedy, J., 1995b. A New Optimizer Using Particle Swarm Theory. Proc. Int. Symp. on Micro Machine and Human Science, Nagoya, Japan, p.29-43.
[8] Eberhart, R.C., Shi, Y., 2001a. Tracking and Optimizing Dynamic Systems with Particle Swarms. Proc. IEEE Congress on Evolutionary Computation, Seoul, Korea, p.94-97.
[9] Eberhart, R.C., Shi, Y., 2001b. Particle Swarm Optimization: Development, Applications and Resources. Proc. IEEE on Evolutionary Computation, Seoul, Korea, p.81-86.
[10] Engell, S., Klatt, K., 1993. Nonlinear Control of a Non-minimum-phase CSTR. Proc. American Control Conf., Los Angeles, p.2041-2045.
[11] Lovbjerg, M., Rasmussen, T.K., Krink, T., 2001. Hybrid Particle Swarm Optimizer with Breeding and Subpopulations. Proc. Third Genetic and Evolutionary Computation Congress, San Diego, USA, p.469-476.
[12] Niu, B., Zhu, Y.L., He, X.X., Zeng, X.P., 2007. MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl. Math. Comput., 185(2):1050-1062.
[13] Nordfeldt, P., Hagglund, T., 2006. Decoupler and PID controller design of TITO systems. J. Process Control, 16(9):923-936.
[14] Shi, Y., Eberhart, R.C., 1998. A Modified Particle Swarm Optimizer. Proc. IEEE Int. Conf. on Evolutionary Computation, Anchorage, Alaska, p.69-73.
[15] Shridhar, R., Cooper, D.J., 1997. A novel tuning strategy for multivariable model predictive control. ISA Trans., 36(4):273-280.
[16] Trierweiler, J.O., Farina, L.A., 2003. RPN tuning strategy for model predictive control. J. Process Control, 13(7):591-598.
[17] Wang, Q.G., 2007. A quasi-LMI approach to computing stabilizing parameter ranges of multi-loop PID controllers. J. Process Control, 17(1):59-72.
[18] Wang, Q.G., Zou, B., Lee, T.H., Qiang, B., 1997. Auto-tuning of multivariable PID controllers from decentralized relay feedback. Automatica, 33(3):319-330.
[19] Wang, Q.G., Zhang, Y., Chiu, M.S., 2003. Non-interacting control design for multivariable industrial processes. J. Process Control, 13(3):253-265.
[20] Wojsznis, W., Gudaz, J., Blevins, T., Mehta, A., 2003. Practical approach to tuning MPC. ISA Trans., 42(1):149-162.
[21] Wood, R.K., Berry, M.W., 1973. Terminal composition control of a binary distillation column. Chem. Eng. Sci., 28(9):1707-1717.
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