TY - GEN
T1 - Offline optimum tuning of the proportional integral controller for speed regulation of a bldc motor through bio-inspired algorithms
AU - Rojas-López, Alam Gabriel
AU - Villarreal-Cervantes, Miguel Gabriel
AU - Rodríguez-Molina, Alejandro
AU - García-Mendoza, Consuelo Varinia
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In this work, a comparative study among different bio-inspired algorithms for offline optimum tuning of a Proportional Integral Controller (PIC) is presented. The PIC regulates the speed of a Brushless Direct Current (BLDC) Motor. The optimum tuning is proposed as a multi-objective optimization problem transformed into a mono-objective problem through a weighted product approach. The performance functions are the Integrated Absolute Error (IAE) and the Average Power. The first one aims to reduce the speed error within the dynamic and the second one aims to reduce energy consumption. The algorithms used to solve the optimization problem are Differential Evolution (DE), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results present a descriptive statistic comparison and the one which has the best behavior among them is presented. Also, a comparison of convergence and execution speed is discussed. The obtained gains found by using the proposed controller tuning present a suitable trade-off in both performance functions even, with dynamic loads with respect to a trial and error tuning procedure.
AB - In this work, a comparative study among different bio-inspired algorithms for offline optimum tuning of a Proportional Integral Controller (PIC) is presented. The PIC regulates the speed of a Brushless Direct Current (BLDC) Motor. The optimum tuning is proposed as a multi-objective optimization problem transformed into a mono-objective problem through a weighted product approach. The performance functions are the Integrated Absolute Error (IAE) and the Average Power. The first one aims to reduce the speed error within the dynamic and the second one aims to reduce energy consumption. The algorithms used to solve the optimization problem are Differential Evolution (DE), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results present a descriptive statistic comparison and the one which has the best behavior among them is presented. Also, a comparison of convergence and execution speed is discussed. The obtained gains found by using the proposed controller tuning present a suitable trade-off in both performance functions even, with dynamic loads with respect to a trial and error tuning procedure.
KW - Bio-inspired approach
KW - Brushless motor
KW - Differential evolution
KW - Firefly algorithm
KW - Offline tuning
KW - Optimization
KW - Particle swarm optimization
UR - http://www.scopus.com/inward/record.url?scp=85096558140&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-62554-2_13
DO - 10.1007/978-3-030-62554-2_13
M3 - Contribución a la conferencia
AN - SCOPUS:85096558140
SN - 9783030625535
T3 - Communications in Computer and Information Science
SP - 169
EP - 184
BT - Telematics and Computing - 9th International Congress, WITCOM 2020, Proceedings
A2 - Mata-Rivera, Miguel Félix
A2 - Zagal-Flores, Roberto
A2 - Barria-Huidobro, Cristian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Congress on Telematics and Computing, WITCOM 2020
Y2 - 2 November 2020 through 6 November 2020
ER -