TY - GEN
T1 - Parameter optimization of tracked vehicle steering control strategy based on particle swarm optimization algorithm
AU - Wang, Yunfeng
AU - Li, Hongcai
AU - Ma, Yue
AU - Hou, Xuzhao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - The electric drive system relies on high-power generators tomeet the electric energy required for vehicle driving and combat, which has become an important prerequisite for the development of future all-electric tanks. In this paper, the control parameters optimization research is carried out on how to improve the control accuracy and stability of the steering control strategy of electric tracked vehicles. The steering control strategy of series hybrid dual-motor coupling drive tracked vehicle based on active disturbance rejection control (ADRC) designed by myself is partially improved, and a control parameter optimization algorithm based on particle swarm optimization (PSO) is designed. The integral of timemultiplied by the absolute value of error criterion (ITAE) is used as the particle swarm optimization algorithm evaluation function to optimize the key control parameters in the steering control strategy to realize the optimization output of the tracked vehicle steering control system. Matlab/ Simulink and Speedgoat semi-physical simulation platform are used to verify the steering control strategy before and after parameter optimization. The comparative test results verify the effectiveness of this parameter optimization.
AB - The electric drive system relies on high-power generators tomeet the electric energy required for vehicle driving and combat, which has become an important prerequisite for the development of future all-electric tanks. In this paper, the control parameters optimization research is carried out on how to improve the control accuracy and stability of the steering control strategy of electric tracked vehicles. The steering control strategy of series hybrid dual-motor coupling drive tracked vehicle based on active disturbance rejection control (ADRC) designed by myself is partially improved, and a control parameter optimization algorithm based on particle swarm optimization (PSO) is designed. The integral of timemultiplied by the absolute value of error criterion (ITAE) is used as the particle swarm optimization algorithm evaluation function to optimize the key control parameters in the steering control strategy to realize the optimization output of the tracked vehicle steering control system. Matlab/ Simulink and Speedgoat semi-physical simulation platform are used to verify the steering control strategy before and after parameter optimization. The comparative test results verify the effectiveness of this parameter optimization.
KW - Parameter optimization
KW - Particle swarm optimization
KW - Semi-physical simulation
KW - Steering control strategy
KW - Tracked vehicles
UR - http://www.scopus.com/inward/record.url?scp=85175067903&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6882-4_38
DO - 10.1007/978-981-99-6882-4_38
M3 - Conference contribution
AN - SCOPUS:85175067903
SN - 9789819968817
T3 - Lecture Notes in Electrical Engineering
SP - 479
EP - 493
BT - Proceedings of 2023 Chinese Intelligent Systems Conference - Volume II
A2 - Jia, Yingmin
A2 - Zhang, Weicun
A2 - Fu, Yongling
A2 - Wang, Jiqiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Chinese Intelligent Systems Conference, CISC 2023
Y2 - 14 October 2023 through 15 October 2023
ER -