Design of Optimal PID Controller Using NSGA-II Algorithm and Level Diagram

This paper introduces a design for multi-objective PID controller using non-dominated sorting genetic algorithm (NSGA-II). When selecting the objectives to be optimized, it is taken into account to cover some important characteristics of the system like performance, robustness and control signals’ smoothness. The decision making is done using Level diagram tool. Three tanks liquid level system control is discussed as a case study. The results show that this tool improves the process of decision making (DM). Also, comparisons with Ziegler and Nichols (Z-N) and different optimization methods are presented.

[1]  Xavier Blasco Ferragud,et al.  A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization , 2008, Inf. Sci..

[2]  Ragab A. El-Sehiemy,et al.  Multiobjective Real-Coded Genetic Algorithm for Economic/Environmental Dispatch Problem, , 2013 .

[3]  L. Kalaivani,et al.  Speed control of switched reluctance motor with torque ripple reduction using non-dominated sorting genetic algorithm (NSGA-II) , 2013 .

[4]  Nitish Katal,et al.  Optimizing the Response of a PID Controller for Three Tank Liquid Level System using Multiobjective Genetic Algorithm , 2012 .

[5]  Leandro dos Santos Coelho,et al.  Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator , 2012, Expert Syst. Appl..

[6]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[7]  M. J. Box A New Method of Constrained Optimization and a Comparison With Other Methods , 1965, Comput. J..

[8]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[9]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[10]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[11]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[12]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[13]  Chen Lei,et al.  The application of GA-based PID parameter optimization for the control of superheated steam temperature , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[14]  Xiang-zhong Guan Multi-objective PID Controller Based on NSGA-II Algorithm with Application to Main Steam Temperature Control , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[15]  Karolin Baecker,et al.  Basic And Advanced Regulatory Control System Design And Application , 2016 .

[16]  Katsuhiko Ogata,et al.  Modern Control Engineering , 1970 .

[17]  Xavier Blasco Ferragud,et al.  Controller Tuning by Means of Multi-Objective Optimization Algorithms: A Global Tuning Framework , 2013, IEEE Transactions on Control Systems Technology.