结合粒子滤波状态观测的滑模自适应主动悬架控制
Sliding Mode Adaptive Active Suspension Control Combined with Particle Filter State Observation
吴晓建 1邹亮 1张铭浩 2江会华 3刘卫东 3胡家琦4
作者信息
- 1. 南昌大学 先进制造学院,江西 南昌 330031;江铃汽车股份有限公司,江西 南昌 330001
- 2. 南昌大学 先进制造学院,江西 南昌 330031
- 3. 江铃汽车股份有限公司,江西 南昌 330001
- 4. 南昌航空大学 测试与光电工程学院,江西 南昌 330038
- 折叠
摘要
主动悬架控制算法面向应用时将面临参数摄动及算法中状态量无法直接获取问题,发展基于状态观测的鲁棒控制算法是关键.本文建立半车侧倾悬架动力学模型,采用非线性滤波函数对悬架动挠度和车身垂向加速度进行协调,再将之与模糊滑模算法相结合,利用模糊逼近实现滑模切换的连续化以改善抖振问题.在此基础上,通过Lyapunov方法进行参数摄动情况下的控制系统稳定性证明及参数自适应律设计.同时,针对算法中无法直接测量的状态量,设计粒子滤波状态观测器以实时估计其状态值.最后,分别在正弦路面激励和随机路面激励等典型工况下进行仿真分析.结果表明:所设计的观测器能够实时准确提供控制算法所需的状态信息,具有参数自适应性的模糊滑模控制器表现出良好的鲁棒适应性,能更大幅度改善车身姿态和平顺性.
Abstract
When applying active suspension control,challenges such as parameter perturbation and the inability to directly acquire state variables in the algorithm may arise.Therefore,developing a robust control algorithm based on state observation is crucial.In this paper,a dynamic model of semi-vehicle roll suspension is established.A nonlinear filtering function coordinates the suspension deflection and the vertical acceleration of the vehicle body.It is then combined with a fuzzy sliding mode algorithm to achieve continuous sliding mode switching by utilizing fuzzy approximation,aiming to improve the chattering problem.On this basis,the stability of the control system under parameter perturbation is proven through the Lyapunov method,and a parameter adaptive law is designed.Additionally,for the state variables that cannot be directly measured in the algorithm,a particle filter state observer is designed to estimate their values in real-time.Finally,simulation analyses are conducted under typical working conditions,such as sinusoidal road excitation and random road excitation.The results demonstrate that the designed observer can provide real-time and accurate state information required by the control algorithm,and the fuzzy sliding mode controller with parameter adaptability exhibits good robustness and greatly improves vehicle posture and ride comfort.
关键词
主动悬架/粒子滤波状态观测器/参数自适应/模糊滑模Key words
active suspension/particle filter state observer/parameter adaptive/fuzzy sliding mode引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据