基于非线性扰动估计的客车防侧翻控制*

doi:10.19562/j.chinasae.qcgc.2020.09.012

摘要/Abstract

摘要： 针对客车防侧翻控制中,实际车辆系统建模容易受到各种未知非线性扰动及参数摄动,难以建立精确的车辆模型,标准滑模控制(sliding mode control, SMC)存在较大抖振等问题,本文中提出径向基神经网络自适应滑模控制(radial basis function-adaptive sliding mode control, RBF-ADSMC)算法。首先,利用RBF神经网络控制器对车辆建模过程中的各种未知扰动项及参数摄动项进行估计;然后,利用RBF神经网络对标准SMC的关键参数进行自适应调节;最后,搭建电控气压硬件在环试验台,对控制算法进行硬件在环试验验证。试验结果表明,RBF-ADSMC算法控制效果良好,能够满足客车防侧翻控制需求。RBF-ADSMC算法与SMC算法相比较,能够减小客车的侧倾角和侧向加速度,提高客车的防侧翻控制效果。

关键词: 车辆工程, 防侧翻控制, 自适应, RBF神经网络, 滑模控制

Abstract: In the anti-rollover control of bus, there are various unknown nonlinear disturbances and parameter perturbations in actual vehicle system modeling process, so it is difficult to establish an accurate vehicle model, and there is problem of big chattering in standard sliding mode control (SMC). The RBF-ADSMC (radial basis function-adaptive sliding mode control, RBF-ADSMC) algorithm is proposed in this paper. Firstly, the radial basis function (RBF) neural network controller is used to estimate various unknown disturbance items and parameter perturbation items in vehicle modeling process. Then, the RBF neural network is used to adaptively adjust the key parameters of the standard SMC. Finally, the electronically controlled pneumatic hardware in the loop test bench is built, and the control algorithm is verified on the hardware in the loop test bench. The test results show that the RBF-ADSMC algorithm has good control effect and can meet the bus rollover control requirements. Compared with the SMC algorithm, the RBF-ADSMC algorithm can reduce the roll angle and lateral acceleration of the bus and improve the anti-rollover control effect of the bus

Key words: vehicle engineering, anti-rollover control, adaptive, RBF neural network, sliding mode control

石求军, 李静. 基于非线性扰动估计的客车防侧翻控制^*[J]. 汽车工程, 2020, 42(9): 1224-1231.

Shi Qiujun, Li Jing. Anti-Rollover Control of Bus Based on Nonlinear Disturbance Estimation[J]. Automotive Engineering, 2020, 42(9): 1224-1231.

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基于非线性扰动估计的客车防侧翻控制^*

Anti-Rollover Control of Bus Based on Nonlinear Disturbance Estimation

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