基于事件触发的汽车主动悬架振动控制器设计

doi:10.12382/bgxb.2023.0590

摘要/Abstract

摘要：

为提升车辆的行驶平稳性和安全性,针对具有输入死区和饱和特性的汽车主动悬架系统,设计一种基于事件触发(Event Trigger, ET)和长短期记忆(Long Short-Term Memory, LSTM)神经网络的智能振动控制器。在构建四分之一车辆主动悬架模型基础上,提出一种高效的ET控制器,能够有效缓解通信紧张问题和避免控制器固有的芝诺现象,提高控制器的稳定性和可靠性。为了进一步增强控制器的智能性和自适应性引入了LSTM神经网络,利用径向基函数神经网络来模拟和生成不同条件下的响应数据实现LSTM神经网络的训练,以精确预测并补偿作动器的死区和饱和影响,进而使主动悬架系统的垂直加速度趋近于0m/s²,从而极大地提升了乘坐舒适性。通过数值仿真验证所设计控制器的适用性和有效性。研究结果表明,所设计的控制器在多种工况下能够有效提高主动悬架系统的动态性能。

关键词: 主动悬架系统, 事件触发, 长短期记忆网络, 径向基函数神经网络

Abstract:

To improve the ride smoothness and safety of vehicles, an intelligent vibration controller based on event trigger (ET) and long short-term memory (LSTM) neural network is devised for automotive active suspension systems characterized by input dead zone and saturation. On the basis of building a quarter-car active suspension model, an appropriate ET controller is proposed, which effectively mitigates the communication bottlenecks and avoids the inherent Zeno phenomenon of controllers, thus enhancing their stability and reliability. A LSTM neural network is introduced to further improve the intelligence and adaptability of controller. A radial basis function neural network is utilized to simulate and generate the required response data for training LSTM neural network and compensate for input dead zone and saturation, making the vertical acceleration of the active suspension system get closer to 0m/s² and thus improve the vehicle ride comfort. The applicability and effectiveness of the designed controller are verified by numerical simulation. The research findings indicate that the controller can effectively enhance the dynamic performance of active suspension system under diverse operating conditions.

Key words: active suspension system, event trigger, long short-term memory network, radial basis function neural network

中图分类号:

U461.4

庞辉, 王明祥, 王磊, 郑理哲. 基于事件触发的汽车主动悬架振动控制器设计[J]. 兵工学报, 2024, 45(8): 2698-2711.

PANG Hui, WANG Mingxiang, WANG Lei, ZHENG Lizhe. Design of Event Trigger-based Vibration Control for Active Suspension System[J]. Acta Armamentarii, 2024, 45(8): 2698-2711.

图/表 21

图1 主动悬架动力学模型

Fig.1 Active suspension system dynamic model

表1 主动悬架系统参数

Table 1 Parameter values of active suspension model

参数	数值
m_s/kg	492
m_u/kg	40
k_s/(N·m^-1)	46021
k_t/(N·m^-1)	238145
c/(N·s·m^-1)	2040

图2 死区和饱和特性

Fig.2 Input dead zone and saturation characteristics

图3 悬架系统控制流程

Fig.3 Suspension system control scheme

图4 长短期记忆神经网络结构图

Fig.4 LSTM neural network structure

图5 径向基函数神经网络结构图

Fig.5 The structure of RBFNN

图6 LSTM控制器构建流程

Fig.6 Construction process of LSTM controller

图7 预测结果对比

Fig.7 Comparison of predicted results

表2 主动悬架系统仿真所需参数

Table 2 Parameter values of active suspension model

符号	数值	符号	数值
q₁	7.792×10⁴	n_in, n_im,n_out	3,5,1
q₂	9.1831×10⁴	I_gc	0.05
q₃	9.6057×10⁴	n_lstm, n_Fcln	200,50
r_lBP	0.2	r_lLSTM	0.01
N_max	200	G_gt	1
N_min	20	r_AR	0.5

图8 凸块路面下主动悬架系统响应对比

Fig.8 Response curves of suspension system on bump road

表3 凸块路面下主动悬架性能指标RMS对比

Table 3 Comparison of RMSs of active suspension performance on bump road

控制器	$z · · s$ RMS	y_uRMS	F RMS
Passive	0.3827	0.002340	243.9
ET-BPNN	0.3637	0.001871	225.6
ET-LSTM	0.3060	0.001821	207.3

表3 凸块路面下主动悬架性能指标RMS对比

Table 3 Comparison of RMSs of active suspension performance on bump road

控制器	$z · · s$ RMS	y_uRMS	F RMS
Passive	0.3827	0.002340	243.9
ET-BPNN	0.3637	0.001871	225.6
ET-LSTM	0.3060	0.001821	207.3

图9 控制器输出

Fig.9 The output of controller

图10 ET控制器触发间隔

Fig.10 Release instants of ET controller

图11 随机路面下悬架系统响应对比

Fig.11 Response curves of suspension system on random road

表4 随机路面下主动悬架性能指标RMS对比

Table 4 Comparison of RMSs of active suspension performance on random road

控制器	$z · · s$ RMS	y_uRMS	F RMS
Passive	0.938	0.01092	567.1
ET-BPNN	0.791	0.008958	399.1
ET-LSTM	0.494	0.008127	263.7

表4 随机路面下主动悬架性能指标RMS对比

Table 4 Comparison of RMSs of active suspension performance on random road

控制器	$z · · s$ RMS	y_uRMS	F RMS
Passive	0.938	0.01092	567.1
ET-BPNN	0.791	0.008958	399.1
ET-LSTM	0.494	0.008127	263.7

图12 控制器输出

Fig.12 The output of controller

图13 ET控制器触发间隔

Fig.13 Release instants of ET controller

图14 速度为5m/s随机路面下悬架系统响应对比

Fig.14 Response curves of suspension system on random road at a speed of 5m/s

图15 速度为10m/s随机路面下悬架系统响应对比曲线

Fig.15 Response curves of suspension system on random road at a speed of 10m/s

图16 速度为15m/s随机路面下悬架系统响应对比曲线

Fig.16 Response curves of suspension system on random road at a speed of 15m/s

表5 不同速度随机路面下主动悬架性能指标RMS对比

Table 5 Comparison of RMSs of active suspension performance on random road at different speeds

车速/ (m·s^-1)	控制算法	RMS( $z · · s$ )	RMS (y_u)	RMS (F)
	Passive	0.469	0.005458	233.5
5	ET-BPNN	0.4354 (↓7.16%)	0.004479 (↓17.9%)	220.9 (↓5.4%)
	ET-LSTM	0.3955 (↓15.67%)	0.003478 (↓36.28%)	199.5 (↓14.56%)
	Passive	0.6633	0.007716	330.3
10	ET-BPNN	0.6157 (↓7.17%)	0.006334 (↓17.9%)	312.5 (↓5.4%)
	ET-LSTM	0.5593 (↓15.68%)	0.004919 (↓36.25%)	282.2 (↓14.56%)
	Passive	0.8124	0.009453	402.5
15	ET-BPNN	0.7641 (↓5.9%)	0.007758 (↓17.93%)	382.7 (↓4.92%)
	ET-LSTM	0.685 (↓15.68%)	0.006024 (↓36.27%)	345.6 (↓14.14%)

表5 不同速度随机路面下主动悬架性能指标RMS对比

Table 5 Comparison of RMSs of active suspension performance on random road at different speeds

车速/ (m·s^-1)	控制算法	RMS( $z · · s$ )	RMS (y_u)	RMS (F)
	Passive	0.469	0.005458	233.5
5	ET-BPNN	0.4354 (↓7.16%)	0.004479 (↓17.9%)	220.9 (↓5.4%)
	ET-LSTM	0.3955 (↓15.67%)	0.003478 (↓36.28%)	199.5 (↓14.56%)
	Passive	0.6633	0.007716	330.3
10	ET-BPNN	0.6157 (↓7.17%)	0.006334 (↓17.9%)	312.5 (↓5.4%)
	ET-LSTM	0.5593 (↓15.68%)	0.004919 (↓36.25%)	282.2 (↓14.56%)
	Passive	0.8124	0.009453	402.5
15	ET-BPNN	0.7641 (↓5.9%)	0.007758 (↓17.93%)	382.7 (↓4.92%)
	ET-LSTM	0.685 (↓15.68%)	0.006024 (↓36.27%)	345.6 (↓14.14%)

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