Employing Particle Swarm Optimizer and Genetic Algorithms for Optimal Tuning of PID Controllers: A Comparative Study

Abstract

The proportional-integral-derivative (PID) controllers were the most popular controllers of this century because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, PID controllers are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. The computational intelligence has purposed genetic algorithms (GA) and particle swarm optimization (PSO) as opened paths to a new generation of advanced process control. These advanced techniques to design industrial control systems are, in general, dependent on achieving optimum performance with the controller when facing with various types of disturbance that are unknown in most practical applications. This paper presents a comparison study of using two algorithms for the tuning of PID-controllers for processes which represents a subsystem of complex industrial processes, known to be non-linear and time variant. Simulation results showed that the PID control tuned by PSO provides an adequate closed loop dynamic for the Ball and Hoop system experiment in wide range operations.

 Keywords: Particle swarm optimisation; genetic algorithms, intelligent control; PID control

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Biography:

Mohammed El-Telbany was born in Dammitta, Egypt, in 1968. He received the B.S. degree in computer engineering and science from the University of Minufia in 1991 and the M.Sc. and PhD degree in Computer Engineering, from Electronics and Communication Department, Cairo University, Faculty of Engineering, in 1997 and 2003 respectively. He has been a Researcher at the Electronics Research Institute since 1993 till now. He has also worked at the ESA at European Space Research Institute (ESRIN), 1998-1999, Frascati, Italy, at the Faculty of Engineering, Al-Ahliyya Amman University, Jordan, 2004-2005 and College of Computer Sciences, King Khalid University, KSA, 2005-2008. He has been involved in the field of autonomous mobile robots and machine leaning. His pervious research includes work on Evolutionary Computation and Forecasting. Current research includes work in robotics and reinforcement learning, and swarm intelligence.

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