A genetic-multivariable fractional order PID control to multi-input multi-output processes

• Published in: Journal of process control Vol. 24; no. 4; pp. 336 - 343
• Main Author: Moradi, Morteza
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2014
• Subjects: Genetic FOPID controller; Multivariable fractional order PID controller; Multivariable genetic-FOPID-process control; Multivariable process control; Multivariable process control; Multivariable fractional order PID controller; Genetic FOPID controller; Multivariable genetic-FOPID-process control
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2014.02.006

•A fractional order PID controller is designed for multivariable processes.•The gains and fractional orders of FOPID controller are tuned using the genetic algorithm.•A new topology for producing and implementing the reproduction, mutation, and crossover algorithms is proposed.•The proposed method is employed on a multivariable process to design the FOPID controller.•The results are compared with H∞ based FOPID and the efficiency of the proposed controller is demonstrated. A multivariable fractional order PID controller is designed and to get suitable coefficients for the controller, a genetic algorithm with a new topology to generate a new population is proposed. The three parts of the genetic algorithm such as reproduction, mutation, and crossover are employed and some variations in the methods are fulfilled so that a better performance is gained. The genetic algorithm is applied to design FOPID controllers for a multivariable process and the results are compared with the responses of a H∞ based multivariable FOPID controller. The simulation responses show that in all cases, the genetic-multivariable FOPID controller has suitable performance, and the output of the system has a smaller error. Also, in the proposed method, variations in one output have a smaller effect on another output which is shown the ability of the proposed method to overcome the interaction in the multivariable processes.