Research Article Open Access

Optimal Control Algorithms for Second Order Systems

Danilo Pelusi1 and Raffaele Mascella1
  • 1 University of Teramo, Italy

Abstract

Proportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.

Journal of Computer Science
Volume 9 No. 2, 2013, 183-197

DOI: https://doi.org/10.3844/jcssp.2013.183.197

Submitted On: 13 October 2012 Published On: 2 April 2013

How to Cite: Pelusi, D. & Mascella, R. (2013). Optimal Control Algorithms for Second Order Systems. Journal of Computer Science, 9(2), 183-197. https://doi.org/10.3844/jcssp.2013.183.197

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Keywords

  • PID Controllers
  • Fuzzy Logic
  • Genetic Algorithms
  • Second Order Plants
  • Neural Networks