泊车机器人气动悬架与举升系统设计与仿真分析

针对超低底盘的抱轮式泊车机器人无悬架系统和无举升机构的问题,设计了气动悬架与气动举升机构,将气动悬架与气动举升机构通过车架连接构成联动系统,使该联动系统具有安装高度低、能自适应不同负载工况和改变举升高度的优点。为提高泊车机器人搬运过程中的平稳性和安全性,建立了气动悬架与气动举升机构联动系统的动力学模型,并设计了车身高度自适应模糊PID控制策略,通过仿真实验结果表明,自适应模糊PID控制策略对负载垂向加速度的改善相较于被动控制达到了40.21%,对气动悬架动位移和负载动位移的改善分别达到了23.28%、14.22%,增强了气动悬架与气动举升机构联动系统对负载的缓冲效果,提高了负载的平稳性和安全性;验证了自适应模糊PID控制策略对该气动悬架与气动举升机构联动系统的有效性。

Performance analysis of air suspension system of heavy truck with semi-active fuzzy control

In order to analyze and evaluate the performance of the air suspension system of heavy trucks with semi-active fuzzy control, a three-dimensional nonlinear dynamical model of a typical heavy truck with 16-DOF（degree of freedom） is established based on Matlab/Simulink software. The weighted root-mean-square（RMS） acceleration responses of the vertical driver ＇s seat, the pitch and roll angle of the cab, and the dynamic load coefficient（DLC） are chosen as objective functions, and the air suspension system is optimized and analyzed by the semi-active fuzzy control algorithm when vehicles operate under different operation conditions. The results show that the influence of the roll angle of the cab on the heavy truck ride comfort is clear when vehicles move on the road surface conditions of the ISO level D and ISO level E at a velocity over 27.5 m/s. The weighted RMS acceleration responses of vertical driver＇ s seat, the pitch and roll angle of the cab are decreased by 24%, 30% and 25%, respectively,when vehicles move on the road surface condition of the ISO level B at a velocity of 20 m/s. The value of the DLC also significantly decreases when vehicles operate under different operation conditions. Particularly, the DLC value of the tractor driver axle is greatly reduced by 27.4% when the vehicle operates under a vehicle fully-loaded condition on the road surface condition of ISO level B at a velocity of 27.5 m/s.

Applying machine learning for cars’semi-active air suspension under soft and rigid roads

To improve the ride quality and enhance the control efficiency of cars’semi-active air suspensions(SASs)under various surfaces of soft and rigid roads,a machine learning(ML)method is proposed based on the optimized rules of the fuzzy control(FC)method and car dynamic model for application in SASs.The root-mean-square(RMS)acceleration of the driver’s seat and car’s pitch angle are chosen as the objective functions.The results indicate that a soft surface obviously influences a car’s ride quality,particularly when it is traveling at a high-velocity range of over 72 km/h.Using the ML method,the car’s ride quality is improved as compared to those of FC and without control under different simulation conditions.In particular,compared with those cars without control,the RMS acceleration of the driver’s seat and car’s pitch angle using the ML method are respectively reduced by 30.20% and 19.95% on the soft road and 34.36% and 21.66% on the rigid road.In addition,to optimize the ML efficiency,its learning data need to be updated under all various operating conditions of cars.

Vehicle height control of electronic air suspension system based on mixed logical dynamical modelling

Due to the coexistence and coupling of continuous variables and discrete events, the vehicle height adjustment process of electronic air suspension system can be regarded as a typical hybrid system. Therefore, the hybrid system theory was applied to design a novel vehicle height control strategy in this paper. A nonlinear mechanism model of the vehicle height adjustment system was established based on vehicle system dynamics and thermodynamic theory for variable-mass gas charge/discharge system. In order to model both the continuous/discrete dynamics of vehicle height adjustment process and the on-off statuses switching of solenoid valves, the framework of mixed logical dynamical(MLD) modelling was used. On the basis of the vehicle height adjustment control strategy, the MLD model of the adjustment process was built by introducing auxiliary logical variables and auxiliary continuous variables. Then, the co-simulation of the nonlinear mechanism model and the MLD model was conducted based on the compiling of HYSDEL. The simulation and experimental results show that the proposed control strategy can not only adjust the vehicle height effectively, but also achieve the on-off statuses direct control of solenoid valves.

Vehicle height and leveling control of electronically controlled air suspension using mixed logical dynamical approach

Vehicle height and leveling control of electronically controlled air suspension(ECAS) still poses theoretical challenges for researchers that have not been adequately addressed in prior research. This paper investigates the design and verification of a new controller to adjust the vehicle height and to regulate the roll and pitch angles of the vehicle body(leveling control) during the height adjustment procedures. A nonlinear mechanism model of the vehicle height adjustment system is formulated to describe the dynamic behaviors of the system. By using mixed logical dynamical(MLD) approach, a novel control strategy is proposed to adjust the vehicle height by controlling the on-off statuses of the solenoid valves directly. On this basis, a correction algorithm is also designed to regulate the durations of the on-off statuses of the solenoid valves based on pulse width modulated(PWM) technology, thus the effective leveling control of the vehicle body can be guaranteed. Finally, simulations and vehicle tests results are presented to demonstrate the effectiveness and applicability of the proposed control methodology.