Abstract
In this chapter, a multivariable controller is tuned by means of a multiobjective optimization design procedure. For this design problem, several specifications are given, regarding individual control loops and overall performance. Due to this fact, a many-objectives optimization problem is stated. In such problems, algorithms could face problems due to the dimensionality of the problem, since their mechanisms to improve convergence and diversity may conflict. Therefore, some guidelines to deal with this optimization process are commented. The aforementioned procedure will be used to tune a multivariable PI controller for the well known Wood and Berry distillation column process using different algorithms.
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References
Berry MW (1973) Terminal composition control of a binary distillation column. Master’s thesis, Department of Chemical and Petroleum Engineering, University of Alberta., Edmonton, Alberta
Das S, Suganthan PN (2010) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 99:1–28
dos Santos Coelho L, Pessôa MW (2011) A tuning strategy for multivariable PI and PID controllers using differential evolution combined with chaotic zaslavskii map. Expert Syst Appl 38(11):13694–13701
Iruthayarajan MW, Baskar S (2009) Evolutionary algorithms based design of multivariable PID controller. Expert Syst Appl 36(5):9159–9167
Iruthayarajan MW, Baskar S (2010) Covariance matrix adaptation evolution strategy based design of centralized PID controller. Expert Syst Appl 37(8):5775–5781
Luyben WL (1986) Simple method for tuning SISO controllers in multivariable systems. Indus Eng Chem Process Des 25:654–660
Menhas MI, Wang L, Fei M, Pan H (2012) Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Syst Appl 39(4):4390–4401
Reynoso-Meza G, Sanchis J, Blasco X, García-Nieto S (2014) Physical programming for preference driven evolutionary multi-objective optimization. Appl Soft Comput 24:341–362
Reynoso-Meza G, Sanchis J, Blasco X, Herrero JM (2012) Multiobjective evolutionary algortihms for multivariable PI controller tuning. Expert Syst Appl 39:7895–7907
Reynoso-Meza G, Sanchis J, Blasco X, Martínez M (2010) Design of continuous controllers using a multiobjective differential evolution algorithm with spherical pruning. In: Applications of evolutionary computation. Springer, pp 532–541
Storn R (2008) SCI: Differential evolution research: trends and open questions, vol LNCS 143. Springer, Heidelberg, pp 1–31
Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
Wood RK, Berry MW (1973) Terminal composition control of a binary distillation column. Chem Eng Sci 28(9):1707–1717
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Reynoso Meza, G., Blasco Ferragud, X., Sanchis Saez, J., Herrero Durá, J.M. (2017). Controller Tuning for Multivariable Processes. In: Controller Tuning with Evolutionary Multiobjective Optimization. Intelligent Systems, Control and Automation: Science and Engineering, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-41301-3_5
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DOI: https://doi.org/10.1007/978-3-319-41301-3_5
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