Optimization of semi-active suspension LQR parameters based on local optimization with a skipping out particle swarm algorithm

C. Y. Yuan; K. T. Li; G. R. Zang; X. C. Wang

doi:10.1117/12.2638950

20 September 2022 Optimization of semi-active suspension LQR parameters based on local optimization with a skipping out particle swarm algorithm

C. Y. Yuan, K. T. Li, G. R. Zang, X. C. Wang

Author Affiliations +

Proceedings Volume 12261, International Conference on Mechanical Design and Simulation (MDS 2022); 122614B (2022) https://doi.org/10.1117/12.2638950
Event: Second International Conference on Mechanical Design and Simulation (MDS 2022), 2022, Wuhan, China

Abstract

Suspension is the general term for all force transmission connections between the frame (or load-bearing body) and the axle (or wheels) [1]. Tomonori used a fuzzy controller with a genetic algorithm for his report [2]. Eskandary and his coworkers. used two airbags working in tandem, one for stiffness adjustment and one for vehicle height adjustment [3]. Castillo designed a PID control by analyzing the non-linear performance of a spherical air suspension and tested and simulated it through simulation [4]. Zhou studied the classification of the vehicle use environment. Particle swarm algorithms were used to optimize air spring parameters for different use environments for vehicles at different speeds and different road classes [5]. Wang used the generalized gradient method to optimize the air springs. The experimental results show that the cab vibration strength can be reduced significantly [6]. This paper investigates quarter-suspension performance optimization. The quarter suspension dynamics is first modelled. The suspension control force is determined by the LQR controller. Secondly, the suspension performance evaluation function is established and an improved particle swarm algorithm is used to replace the manual inexperience in determining the LQR controller parameter drawbacks. The effectiveness of the LQR controller for suspension control is improved. Finally, the improved optimized control effect is compared to the passive suspension and the unimproved algorithm control effect for comparison, and it is concluded that the improved algorithm has significantly improved the smoothness of the LQR controller controlled suspension.

Citation Download Citation

C. Y. Yuan, K. T. Li, G. R. Zang, and X. C. Wang "Optimization of semi-active suspension LQR parameters based on local optimization with a skipping out particle swarm algorithm", Proc. SPIE 12261, International Conference on Mechanical Design and Simulation (MDS 2022), 122614B (20 September 2022); https://doi.org/10.1117/12.2638950

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KEYWORDS

Particles

Optimization (mathematics)

Algorithms

Particle swarm optimization

Computer simulations

Annealing

Device simulation

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