Research on Vehicle Motion Control Strategy Based on Machine Vision

Author(s):  
Jianping Mo ◽  
Haijiang Lan
2021 ◽  
Author(s):  
Han Zhang ◽  
Changzhi Zhou ◽  
Chunyan Wang ◽  
Wanzhong Zhao

Abstract This paper presents an energy efficient control strategy for electric vehicle (EV) driven by in-wheel-motors (IWMs) based on discrete adaptive sliding mode control (DASMC). The nonlinear vehicle model, tire model and the IWM model are established at first to represent the operation mechanism of the whole system. Based on the modeling, two virtual control variables are used to represent the longitudinal and yaw control efforts to coordinate the vehicle motion control. Then DASMC method is applied to calculate the required total driving torque and yaw moment, which can improve the tracking performance as well as the system robustness. According to the vehicle nonlinear model, the additional yaw moment can be expressed as a function of longitudinal and lateral tire forces. For further control scheme development, a tire force estimator using unscented Kalman filter is designed to estimate real-time tire forces. On these bases, energy efficient torque allocation method is developed to distribute the total driving torque and differential torque to each IWM, considering the motor energy consumption, the tire slip energy consumption and the brake energy recovery. Simulation results of the proposed control strategy using co-platform of Matlab/Simulink and CarSim® demonstrate that it can accomplish the vehicle motion control in a coordinated and economic way.


2011 ◽  
Vol 130-134 ◽  
pp. 309-312 ◽  
Author(s):  
Ze Yu Chen ◽  
Guang Yao Zhao

Based on tracked vehicle dynamics analysis, a fuzzy control strategy is proposed in this paper for the dual electric tracked vehicle motion control. The inputs of fuzzy system are driver acceleration, braking and steering signals besides vehicle velocity feedback signal, while outputs are dual motors’ torque commands and mechanical braker’s target force. Control strategy contains two fuzzy logics, one is for steering and straight-line running control, the other is for braking control section. Simulation results show that the fuzzy control strategy presented here is correct and effective for electric tracked vehicle motion control.


2021 ◽  
Author(s):  
Arpan Chatterjee ◽  
Perry Y. Li

Abstract The Hybrid Hydraulic-Electric Architecture (HHEA) was proposed in recent years to increase system efficiency of high power mobile machines and to reap the benefits of electrification without the need for large electric machines. It uses a set of common pressure rails to provide the majority of power hydraulically and small electric motors to modulate that power for precise control. This paper presents the development of a Hardware-in-the-loop (HIL) test-bed for testing motion control strategies for the HHEA. Precise motion control is important for off-road vehicles whose utility requires the machine being dexterous and performing tasks exactly as commanded. Motion control for the HHEA is challenging due to its intrinsic use of discrete pressure rail switches to minimize system efficiency or to keep the system within the torque capabilities of the electric motor. The motion control strategy utilizes two different controllers: a nominal passivity based back-stepping controller used in between pressure rail switches and a transition controller used to handle the event of a pressure rail switch. In this paper, the performance of the nominal control under various nominal and rail switching scenarios is experimentally evaluated on the HIL testbed.


2019 ◽  
Vol 107 ◽  
pp. 1-14 ◽  
Author(s):  
Chongfeng Wei ◽  
Richard Romano ◽  
Natasha Merat ◽  
Yafei Wang ◽  
Chuan Hu ◽  
...  

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