Abstract
A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a
PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a wellperforming controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior
knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications.
The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.
PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a wellperforming controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior
knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications.
The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.
Original language | English |
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Title of host publication | Proceedings of the IEEE Congress on Evolutionary Computation 2017 |
Publisher | IEEE Computer Society Press |
Pages | 1819-1826 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-5090-4601-0 |
ISBN (Print) | 978-1-5090-4602-7 |
DOIs | |
Publication status | Published - 7 Jul 2017 |
Event | IEEE Congress on Evolutionary Computation 2017 - Donostia-San Sebastian, Spain Duration: 5 Jun 2017 → 8 Jun 2017 |
Conference
Conference | IEEE Congress on Evolutionary Computation 2017 |
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Abbreviated title | CEC 2017 |
Country/Territory | Spain |
City | Donostia-San Sebastian |
Period | 5/06/17 → 8/06/17 |