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车辆阻尼多模式切换半主动空气悬架系统分析及其优化控制研究
Analysis and Optimal Control of Vehicle Semi-active Air Suspension System Based on Damping Multi-mode Switching
【作者】 马瑞;
【导师】 陈龙;
【作者基本信息】 江苏大学 , 车辆工程, 2020, 硕士
【摘要】 半主动空气悬架通过实时调节减振器阻尼状态,从而能够有效满足车辆在大范围运行工况下的隔振性能要求,已成为车辆悬架领域的研究热点之一。然而,现有的节流口面积可调式和油液粘度可调式阻尼减振器尽管从功能角度出发,已经能够实现良好的阻尼调节性能,但是从实际应用角度考虑,仍存在成本高、设计复杂以及能耗偏大等问题。据此,本文提出一种基于高速开关电磁阀的车辆新型阻尼多模式切换半主动空气悬架系统,并针对其阻尼调节特性与阻尼控制策略进行分析和研究,以期实现半主动空气悬架系统性能的进一步提升。论文具体研究内容包括以下方面:首先,完成了车辆新型阻尼多模式切换半主动空气悬架系统的结构设计与模型构建。分析了基于高速开关电磁阀的半主动空气悬架系统结构特征及其工作原理,结合流体力学和空气动力学理论,建立了半主动空气悬架系统数学模型,在此基础上,进一步根据目标车辆参数,确定了空气弹簧刚度、悬架系统最佳阻尼比以及减振器各档位阻尼系数等系统关键参数。其次,进行了车辆新型阻尼多模式切换半主动空气悬架系统的力学特性仿真与试验验证。基于MATLAB搭建了系统仿真模型,仿真分析了阻尼调节装置关键参数对减振器阻尼特性的影响规律,进而在此基础上,结合前述各档位阻尼系数划分完成了阻尼调节装置主要结构参数的确定。试制了阻尼多模式切换半主动空气悬架系统样机,并进行了样机台架试验,验证了系统力学特性试验结果与仿真的一致性。再次,实现了车辆新型阻尼多模式切换半主动空气悬架系统的阻尼控制策略设计。在建立车辆二自由度垂向振动模型的基础上,采用模糊神经网络控制算法设计了系统阻尼多模式切换控制策略,制定了系统模糊控制逻辑,完成了基于BP神经网络的模糊控制优化设计,仿真分析了阻尼控制性能,仿真结果表明,基于阻尼多模式切换控制策略的车辆半主动空气悬架系统能够显著提升系统隔振性能。最后,完成了车辆新型阻尼多模式切换半主动空气悬架系统的阻尼控制性能台架试验,结合dSPACE快速控制原型实现了系统阻尼控制算法,利用单通道液压伺服激振试验台进行了系统台架试验,最终验证了系统阻尼控制策略的有效性。
【Abstract】 The semi-active air suspension can effectively meet the requirements of vibration isolation performance in a wide range of operating conditions by adjusting the damping state of the dampers in real time,and has become one of the research hotspots in the field of vehicle suspension.However,although the dampers with adjustable orifice area and adjustable oil viscosity have been able to achieve good damping performance from the functional point of view,there are still problems such as high cost,complex design and high energy consumption from the practical application angle.Accordingly,this paper proposes a new type of semi-active air suspension system with damping multi-mode based on high-speed solenoid valves,and analyzes its damping adjustment characteristics and damping control strategy.Specific research for the paper included the following:Firstly,the structural design and model construction of the semi-active air suspension system based on damping multi-mode switching is completed.The mathematical model of the semi-active air suspension system is established by analyzing the working principle and structural characteristics of the semi-active air suspension system based on high-speed switching solenoid valves.On this basis,system parameters such as the stiffness of the air spring,the optimal damping ratio of suspension system and damping coefficient in each gear are determined according to the target vehicle parameters.Secondly,the simulation of mechanical properties and experimental verification of the new semi-active air suspension based on damping multi-mode switching are carried out.Based on MATLAB,a simulation model of suspension system is constructed to analyze the influence of the key parameters of the damping regulation device on the damping characteristics of the damper.On this basis,the main structural parameters of the damping regulation device are determined.A prototype of the semi-active air suspension system with damping multi-mode is produced,and the prototype test is conducted to verify the consistency of the mechanical characteristics test results with the simulation.Thirdly,the control strategy of damping multi-mode switching which based on fuzzy neural network control logic is designed.Based on the vertical vibration model,the system fuzzy control logic is formulated,and the optimization design of fuzzy control based on BP neural network is completed.The damping control performance is analyzed by simulation,the simulation results show that the vehicle semi-active air suspension system based on damping multi-mode switching control strategy can significantly improve the system vibration isolation performance.Finally,the bench test of the semi-active air suspension system based on damping multi-mode is completed,and the system damping control algorithm is implemented in combination with the dSPACE rapid control prototype.The effectiveness of the fuzzy neural network control strategy is demonstrated by conducting the system bench test.
【Key words】 Semi-active air suspension; Adjustable damping shock absorber; Mode switching; Fuzzy neural network; Rapid prototype;