TY - GEN
T1 - Comparison of Bioinspired Optimization Techniques for Improving the Performance of Dynamic Sliding Mode Controllers
AU - Espin, Jorge
AU - Estrada, Sebastian
AU - Benitez, Diego
AU - Camacho, Oscar
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Bio-inspired optimization algorithms have been shown in recent years to produce optimal solutions to various complex computational issues in science and engineering. This paper presents a comparative examination of bio-inspired algorithms to better understand and measure their efficacy in searching for the best tuning parameters for a Dynamical Sliding Mode Control for integrating systems with an inverse response and dead time. The comparative analysis includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms, and how they can improve the performance of the controller by searching for optimum tuning parameter solutions. The parameters of each algorithm have different impacts on the searching mechanism and were evaluated in two simulated systems. Ant colony optimization shows a significant capability over other algorithms to find optimal solutions for our problem.
AB - Bio-inspired optimization algorithms have been shown in recent years to produce optimal solutions to various complex computational issues in science and engineering. This paper presents a comparative examination of bio-inspired algorithms to better understand and measure their efficacy in searching for the best tuning parameters for a Dynamical Sliding Mode Control for integrating systems with an inverse response and dead time. The comparative analysis includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms, and how they can improve the performance of the controller by searching for optimum tuning parameter solutions. The parameters of each algorithm have different impacts on the searching mechanism and were evaluated in two simulated systems. Ant colony optimization shows a significant capability over other algorithms to find optimal solutions for our problem.
KW - Bioinspied optimization algorithms
KW - Dynamical Sliding Mode Control
KW - dead time
KW - integrating systems
KW - inverse response
UR - http://www.scopus.com/inward/record.url?scp=85141408547&partnerID=8YFLogxK
U2 - 10.1109/ColCACI56938.2022.9905285
DO - 10.1109/ColCACI56938.2022.9905285
M3 - Contribución a la conferencia
AN - SCOPUS:85141408547
T3 - 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings
BT - 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022 - Proceedings
A2 - Orjuela-Canon, Alvaro David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022
Y2 - 27 July 2022 through 29 July 2022
ER -