Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the tradition...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8204 |
| _version_ | 1797590428001763328 |
|---|---|
| author | Weipeng Zhao Liang Gu |
| author_facet | Weipeng Zhao Liang Gu |
| author_sort | Weipeng Zhao |
| collection | DOAJ |
| description | In this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system. |
| first_indexed | 2024-03-11T01:20:19Z |
| format | Article |
| id | doaj.art-1f0b16761be54911b07aa64682fe0007 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:19Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1f0b16761be54911b07aa64682fe00072023-11-18T18:09:47ZengMDPI AGApplied Sciences2076-34172023-07-011314820410.3390/app13148204Hybrid Particle Swarm Optimization Genetic LQR Controller for Active SuspensionWeipeng Zhao0Liang Gu1Beijing Institute of Technology, No. 5 Yard, Zhongguancun South Street, Haidian District, Beijing 100081, ChinaBeijing Institute of Technology, No. 5 Yard, Zhongguancun South Street, Haidian District, Beijing 100081, ChinaIn this paper, a hybrid particle swarm optimization genetic algorithm LQR controller is used on a quarter car model with an active suspension system. The proposed control algorithm is utilized to overcome the shortcoming that the weight matrix Q and matrix R determined by experience in the traditional LQR control method. The proposed hybrid control method makes it possible to achieve the optimal control effect. A full-order state observer is proposed to observe the state of active suspension. A quarter car active suspension model and road input model are presented at first, and the LQR controller based on the hybrid particle swarm optimization genetic algorithm is utilized in the active suspension system control. Sprung mass acceleration, suspension deflection, and tire dynamic load are selected as the control effect evaluation index. Next, simulation results are presented. According to the results, compared with the passive suspension and active suspension with a traditional LQR control, there is an obvious reduction in the sprung mass acceleration, deflection, and tire dynamic load with an optimized controller under case 1 and case 2. Simultaneously, the system state fed back by the full-order state observer can effectively reflect the true state of the active suspension system.https://www.mdpi.com/2076-3417/13/14/8204full-order state observeractive suspensionparticle swarmgeneticLQR |
| spellingShingle | Weipeng Zhao Liang Gu Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension Applied Sciences full-order state observer active suspension particle swarm genetic LQR |
| title | Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension |
| title_full | Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension |
| title_fullStr | Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension |
| title_full_unstemmed | Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension |
| title_short | Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension |
| title_sort | hybrid particle swarm optimization genetic lqr controller for active suspension |
| topic | full-order state observer active suspension particle swarm genetic LQR |
| url | https://www.mdpi.com/2076-3417/13/14/8204 |
| work_keys_str_mv | AT weipengzhao hybridparticleswarmoptimizationgeneticlqrcontrollerforactivesuspension AT lianggu hybridparticleswarmoptimizationgeneticlqrcontrollerforactivesuspension |