Speed-adaptive motion control algorithm for differential steering vehicle

Abstract

The differential steering vehicle uses the in-wheel motors to drive the wheels directly and individually. However, in order to deliver the required structural robustness, the differential steering vehicle discarded the mechanical steering system and achieved vehicle steering by applying differential speed between the left and right wheels. This paper presents a novel speed-adaptive motion control algorithm based on the unique chassis configuration to enhance the performance in vehicle handling and lateral stability. The proposed control method first estimates the driver’s driving intention, from which reference wheel speeds are individually generated for each wheel based on their respective slipping and skidding status. Finally, the torque command is automatically adjusted by the speed-tracking controller by proactive adaptation of the wheel spinning resistance to effectively avoid excessive slip on low friction. This method exploits the fact that the torque at the steady state will always be equal to the available longitudinal force, thereby delivering the sophisticated tire force estimation without additional computational efforts, and ultimately helping conserve energy and promote lateral stability. The essential road information collected by this algorithm is also made available to drivers for improved vehicle controllability. The algorithm is subjected to three experimental case scenarios—pivot steering, low-friction driving, and curvilinear driving—through TruckSim–Simulink co-simulations and real vehicle tests, and the results have validated the algorithm in realizing the high-performance steering and maneuvering capabilities for six-wheeled differential steering vehicles.

Metalearning-Based Fault-Tolerant Control for Skid Steering Vehicles under Actuator Fault Conditions

“…Kinematic methods are simple to implement but cannot effectively prevent the independently driving wheels from slipping. Kinematic methods in applications usually ignore wheel slip [ 10 , 11 ] or simply use the wheel speed following controller [ 12 , 13 ] to predict and compensate for such slippages.…”

Development of the peanut precision fertilization control system based on threshold velocity algorithm

“…Main controller Figure 1 System structure diagram configure the timer to output the PWM control signal with the corresponding duty cycle. The motor driver, based on the PWM signal received, controls the motor speed, and at the same time, the encoder collects the real-time motor speed and performs fuzzy PID control on the motor according to the difference between the actual and the theoretical speeds to accomplish precise fertilization [24][25][26] .…”

An anti-slip control strategy with modifying target and torque reallocation for heavy in-wheel motor vehicle

“…T Y is not maintained in the states. The torque reallocation strategy in these states is expressed as the formulas (33) and (34).…”

“…33 For the non-steering system vehicle equipped with in-wheel motors, a speed-adaptive motion control algorithm is proposed to achieve vehicle steering, and with a speedtracking control in the algorithm, the driving torque is automatically adjusted to effectively avoid excessive slipping on low friction. 34 Iterative learning control is widely applied to the robotic control, which gradually learns and adapts to the load, and which can achieve the function of the traditional integral control with unknown parameters. [35][36][37] In addition, the stability can be proved theoretically.…”

“…33 For the non-steering system vehicle equipped with in-wheel motors, a speed-adaptive motion control algorithm is proposed to achieve vehicle steering, and with a speed-tracking control in the algorithm, the driving torque is automatically adjusted to effectively avoid excessive slipping on low friction. 34…”