TY - GEN
T1 - A Differential Particle Scheme with Successful Parent Selection and its Application to PID Control Tuning
AU - Parque, Victor
N1 - Funding Information:
This research was supported by JSPS KAKENHI 20K11998.
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Proportional-integral-derivative (PID) control is ubiquitous in industrial automation tasks, and the parameter tuning of the gains is challenging due to nonlinearity and stagnation in local optima. In this paper we present a differential particle scheme based on stagnation-based selection mechanism, and evaluate its effectiveness in the stabilization of a nonlinear inverted pendulum and a magnetic levitation system. Our computational experiments show the feasibility to avoid stagnation, the lower variability of convergence over independent runs, and the feasibility to converge to significantly better fitness values compared to relevant heuristics in the literature. We believe our approach offers the building blocks to build stagnation-free nature inspired optimization algorithms useful for adaptive control and tuning.
AB - Proportional-integral-derivative (PID) control is ubiquitous in industrial automation tasks, and the parameter tuning of the gains is challenging due to nonlinearity and stagnation in local optima. In this paper we present a differential particle scheme based on stagnation-based selection mechanism, and evaluate its effectiveness in the stabilization of a nonlinear inverted pendulum and a magnetic levitation system. Our computational experiments show the feasibility to avoid stagnation, the lower variability of convergence over independent runs, and the feasibility to converge to significantly better fitness values compared to relevant heuristics in the literature. We believe our approach offers the building blocks to build stagnation-free nature inspired optimization algorithms useful for adaptive control and tuning.
KW - Differential evolution
KW - Gain tuning
KW - Optimization
KW - PID control
KW - PID tuning
KW - Particle swarm optimization
KW - Stagnation
UR - http://www.scopus.com/inward/record.url?scp=85111012081&partnerID=8YFLogxK
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U2 - 10.1109/CEC45853.2021.9504971
DO - 10.1109/CEC45853.2021.9504971
M3 - Conference contribution
AN - SCOPUS:85111012081
T3 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
SP - 522
EP - 529
BT - 2021 IEEE Congress on Evolutionary Computation, CEC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Congress on Evolutionary Computation, CEC 2021
Y2 - 28 June 2021 through 1 July 2021
ER -