TY - GEN
T1 - Optimizing a Dynamic Sliding Mode Controller with Bio-Inspired Methods
T2 - 5th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2022
AU - Espin, Jorge
AU - Estrada, Sebastian
AU - Benítez, Diego S.
AU - Camacho, Oscar
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/3/31
Y1 - 2023/3/31
N2 - In the past few years, bio-inspired optimization algorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating systems with an inverse response and dead time. The comparison includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms. It shows how they can improve the performance of the controller by looking for the best tuning parameter solutions. The parameters of each algorithm affect the searching mechanism in different ways, and these effects were tested in two simulated systems. Ant colony optimization is much better than other algorithms at finding the best answers to our problems.
AB - In the past few years, bio-inspired optimization algorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating systems with an inverse response and dead time. The comparison includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms. It shows how they can improve the performance of the controller by looking for the best tuning parameter solutions. The parameters of each algorithm affect the searching mechanism in different ways, and these effects were tested in two simulated systems. Ant colony optimization is much better than other algorithms at finding the best answers to our problems.
KW - Bioinspired optimization algorithms
KW - Dead time
KW - Dynamical Sliding Mode Control
KW - Integrating systems
KW - Inverse response
UR - https://www.scopus.com/pages/publications/85152566200
U2 - 10.1007/978-3-031-29783-0_5
DO - 10.1007/978-3-031-29783-0_5
M3 - Contribución a la conferencia
AN - SCOPUS:85152566200
SN - 9783031297823
T3 - Communications in Computer and Information Science
SP - 63
EP - 80
BT - Applications of Computational Intelligence - 5th IEEE Colombian Conference, ColCACI 2022, Revised Selected Papers
A2 - Orjuela-Cañón, Alvaro David
A2 - Lopez, Jesus
A2 - Arias-Londoño, Julian David
A2 - Figueroa-García, Juan Carlos
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 July 2022 through 29 July 2022
ER -