A brief summary of the motion control direction of distributed drive unmanned vehicles

In recent years, driven by the concept of the Internet of Everything, unmanned vehicles have become an emerging research hotspot in both academia and industry due to their expected considerable economic benefits and market value. In addition, the "electrification" in the new four modernizations of automobiles requires vehicles to be driven by electric motors instead of traditional engines. Among them, electric drive can be divided into centralized drive and distributed drive, and distributed drive can be divided into wheel motor drive and hub motor drive. In-wheel motors have attracted widespread attention at home and abroad for their features such as easy driving/braking, anti-skid, active yaw control, differential power steering, vehicle body motion attitude control, short transmission chain, and high driving efficiency. This literature research is mainly aimed at the direction of motion control of unmanned vehicles driven by in-wheel motors. The unmanned vehicle driven by the hub motor is mainly carried out in the direction of motion control from three aspects.

1 Influence of the application of in-wheel motors on vehicle stability

The application of in-wheel motors in the vehicle drive system greatly simplifies the transmission system of the vehicle, cancels the mechanical structures such as transmission half shafts and differentials, and improves the transmission effect of the vehicle. However, when one side of the motor fails due to an unexpected failure and the driving torque of the other side cannot be reduced instantaneously, the vehicle is likely to yaw violently or even spin sharply due to the unequal driving force on both sides. Zhang Lei et al. constructed a control method switching rule based on the vehicle's estimated deviation distance and additional yaw rate, which not only ensures the vehicle's dynamics and yaw stability, but also improves the execution efficiency of the control strategy. Luo Yugong et al. developed a rule-based drive system failure control algorithm based on different failure conditions and vehicle states. In order to solve the yaw problem caused by motor failure during path tracking, GUO[ et al. proposed an adaptive fast terminal sliding mode fault-tolerant control method. The failure of the driving motor is very accidental. After the failure of the driving motor at different positions, the vehicle state changes are different. WANG et al. proposed an active fault diagnosis method, which can predict the failure of the driving wheel in a variety of scenarios. Control gain. The above literatures all rely heavily on the vehicle dynamics model. LUO et al. proposed an active fault-tolerant control method based on a new dynamics linearization technique based on pseudo partial derivatives. In the design of the control system, only input and output data are required without considering the drive system. synergy, steering system or precision of the model.
Since the complete consistency of the driving motor cannot be guaranteed during the manufacturing process, the parameter error and unbalanced force of the driving motor will also affect the driving performance and stability control function of the vehicle.
The introduction of in-wheel motors will undoubtedly increase the unsprung mass, increase the impact of tire dynamic load, and deteriorate the handling stability. The solutions to this problem include: lightweight design of wheel rim system, innovative design of wheel rim configuration and dynamic vibration reduction method [etc.

2 Control advantage of in-wheel motor drive on vehicle dynamics

Hub motors have the advantages of fast response, precise torque output, and equivalent positive and negative torque output, and have great advantages in vehicle driving, start-stop, and stability control. In-wheel motor-driven vehicles can independently control the torque and speed of each wheel. Zhang Lipeng et al. have developed a motor torque adaptive motor suitable for distributed drive electric vehicles to solve the problem that the inconsistent driving force of tires on low-attached roads can easily lead to vehicle instability. The drive anti-skid controller effectively ensures the lateral stability of distributed drive electric vehicles. However, this method assumes that the optimal slip ratio of the tire is a constant value in advance, and does not consider the influence of the adhesion coefficient on the optimal slip ratio. In order to more accurately ensure that the wheels are in the best slip ratio, Xiong Lu et al. designed a fuzzy adaptive road surface adhesion coefficient fusion estimation method, using the longitudinal/lateral excitation of the vehicle to realize the algorithm's estimation adaptability to different driving conditions of the vehicle . Arash et al. combined extended Kalman filter, recursive least squares method and neural network algorithm to design a road adhesion coefficient estimation method. Han et al. proposed a method for estimating the longitudinal tire-road adhesion coefficient, which considered the unstructured road surface.
After solving the problem of vehicle drive anti-skid, the vehicle yaw moment control can be realized more efficiently through the differential drive of the in-wheel motors. At this time, the key issue is the distribution strategy of the drive torque. Chen Wuwei et al. designed three control modes based on the hierarchical control strategy, and used the pseudo-inverse optimization algorithm to optimize the distribution of the driving torque of each wheel to ensure the yaw stability and reduce the driving energy consumption of the vehicle. Based on the bicycle model, Kobayashi et al. considered the steering resistance torque and power loss in the process of driving torque distribution, and optimized the energy consumption of the whole vehicle. Zhang Lipeng et al. used the differential drive of the in-wheel motors to decouple the roll and yaw motion of the vehicle, effectively controlling the roll motion while ensuring the yaw stability, thereby greatly improving the spatial stability of the vehicle.

3 Coupling effect of in-wheel motor drive and other control systems

As a typical overdrive system, an electric vehicle driven by an in-wheel motor can have multiple control methods for a certain state quantity. At the same time, there are overlapping areas between the control domains of each actuator. In the integrated design of the vehicle control system, it is necessary to Fully consider the coupling superposition effect between control systems.
Influence of in-wheel motor differential drive on the steering system: Yu Zhuoping analyzed the feasibility of differential power steering based on the characteristics and topological structure of the front-wheel drive of distributed drive electric vehicles, and proposed a closed-loop control method for differential power steering. Improved steering ease and back-to-center vehicle response. Wang Qidong et al. established a dynamic model between differential torque and differential angle based on the rack and pinion steering gear, which facilitates further and more precise dynamic control.
Influence of wheel hub motor differential drive on roll angle: The special space topology of the wheel hub motor drive vehicle makes the reaction force of the wheel hub motor differential drive provide a certain roll control torque to the body through the suspension guide bar system.
Coupling of hub motor differential drive and steering wheel angle during yaw rate control: The yaw rate control of the vehicle can generally be realized by steering wheel angle correction and differential drive/braking. Although the control methods of the two are different, But its essence is to control the longitudinal/lateral force of the tire, so there is a certain coupling effect between AFS and DYC in control. Most of the control methods currently used are: when the tire lateral force is in the linear range, AFS intervenes in stability control; when the tire enters the non-linear range, DYC intervenes in control or AFS and DYC cooperative control.
Coupling of in-wheel motor differential braking, steering wheel angle, and active suspension during roll angle control: There are two ways to control the roll angle of the vehicle. One is to provide roll control torque to suppress the roll angle, which is generally realized by active suspension[; It is to reduce the lateral acceleration and indirectly control the body roll angle, which is generally realized through differential braking and steering wheel angle correction.

4 Determination of vehicle stability control domain

The phase diagram theory is often used to judge the stability of the vehicle. Shoji et al. used the side slip angle of the vehicle center of mass and the side slip angle velocity of the center of mass to construct a phase plane to analyze the lateral stability of the vehicle. They believed that the phase plane ratio was determined by the side slip angle of the vehicle center of mass The phase plane formed with the yaw rate is more suitable for analyzing the lateral stability characteristics of the vehicle. Xiong Lu and Kazemi analyzed the vehicle stability through the phase plane. Chen et al. proposed a method for estimating the stability domain of the vehicle. This method does not need to distinguish the stability of the vehicle and the tire. Compared with the commonly used phase plane, it is more conservative in estimating the stability. Based on the proposed stability control method, a lot of research has been done. research work.
In addition to using the phase diagram theory to judge the stability, the expected center of mass side slip angle and yaw angular velocity of the vehicle under general working conditions are also given through the combination of theory and experiment. superior.

5 Integrated control of in-wheel motor differential drive and AFS, ASS and other systems

With the development of automotive electronic control systems, the modularization of vehicle structures and the integration of control systems are getting higher and higher. The integration of existing decentralized independent controllers into domain controllers and an open overall architecture has become The development trend of today's automotive electrical system. Top-down and hierarchical coordination are the two most commonly used centralized control architectures. Each subsystem is coordinated through a dynamic inverse model, and the control allocation method based on constraint optimization coordinates the actuators of the drive system.
Based on a layered architecture, ZHAO et al. integrated active suspension, active front wheel steering and direct yaw moment control. The upper-layer controller is used to calculate the output torque of the active suspension and the middle-layer controller; the middle-layer controller controls the triggering of the lower-layer actuators based on the scope of AFS and DYC; the lower-layer controller tracks the expected value of the state quantity according to the control signals of the upper and middle layers. Experimental results show that the proposed hierarchical control method can improve the lateral and longitudinal dynamics of the vehicle. HE et al. used AFS to improve the steering ability of the vehicle at low lateral acceleration, dynamic stability control (DSC) was used to improve the chassis stability of the vehicle under extreme conditions, and finally proposed a rule-based vehicle performance Optimization. XIAO et al. also proposed an integrated control method based on the hierarchical control strategy, the upper layer is collaborative planning, and the lower layer is bottom-layer execution. When the vehicle is in the critical condition, the computing power of its controller needs to be significantly higher than that in the normal condition, otherwise the vehicle is likely to be unstable. Eman et al. used integral and non-singular fast terminal sliding mode control strategies to improve the transient response capability of the vehicle. The simulation results show that the proposed control strategy also has strong robustness.

6 Other Issues in Vehicle Kinematics Control

There are many factors to be considered in the kinematic control of the vehicle. In addition to the above systematic literature research, there are other issues that need to be focused on, such as the stability control problem when the longitudinal/lateral force of the tire reaches saturation. The trajectory tracking problem of human-driven vehicle stability control and the optimization of vehicle energy consumption during stability control.
Both WANG and CHEN established the loss function of the tire driving force distribution, and weighted the tire longitudinal driving force and the longitudinal force increment to avoid the saturation of the tire driving force. Liu Li et al. [45] proposed a new full-wheel longitudinal allocation algorithm for active dynamic target adjustment. There is a certain antagonistic relationship between the stability control and trajectory tracking of unmanned vehicles. How to study the synergistic relationship between the two according to different working conditions on structured roads still needs further research.
Prospects for Kinematics Control of Distributed Drive Unmanned Vehicles
A large number of literatures have studied vehicle stability control methods based on working conditions. The stability control method needs to be further studied. The control idea is to integrate the stability control into the real-time path planning algorithm, and complete the end-to-end path planning of the vehicle based on the segmental limit of the stability control. Since the intervention of stability control will inevitably affect the ride comfort of the vehicle, the goal of this kind of stability control in all working conditions is to optimize the driving trajectory of the vehicle under the premise of reducing the number of times of stability control intervention as much as possible. The ability to predict environmental information is required, and finally achieve the application coordination of path planning, vehicle dynamics and stability control systems.