Comparison of Typical Controllers for Direct Yaw Moment Control Applied on an Electric Race Car

Abstract

Direct Yaw Moment Control (DYC) is an effective way to alter the behaviour of electric cars with independent drives. Controlling the torque applied to each wheel can improve the handling performance of a vehicle making it safer and faster on a race track. The state-of-the-art literature covers the comparison of various controllers (PID, LPV, LQR, SMC, etc.) using ISO manoeuvres. However, a more advanced comparison of the important characteristics of the controllers’ performance is lacking, such as the robustness of the controllers under changes in the vehicle model, steering behaviour, use of the friction circle, and, ultimately, lap time on a track. In this study, we have compared the controllers according to some of the aforementioned parameters on a modelled race car. Interestingly, best lap times are not provided by perfect neutral or close-to-neutral behaviour of the vehicle, but rather by allowing certain deviations from the target yaw rate. In addition, a modified Proportional Integral Derivative (PID) controller showed that its performance is comparable to other more complex control techniques such as Model Predictive Control (MPC).

Effect of Weight Distribution and Active Safety Systems on Electric Vehicle Performance

“…Furthermore, Equation (3) leads to the same conclusion as at constant lateral forces and steering angle, increasing the value of a leads to a higher turning moment (i.e., oversteer). The same reasoning can be applied by looking at the understeer gradient in Equation (11), as it expresses the cornering behavior as a function of the static vertical load. Increasing the vertical front load (i.e., reducing the parameter a) leads to understeering, whereas decreasing it leads to oversteering.…”

“…Benoit Lacroix et al conducted a study to compare different methods on direct yaw moment control (i.e., PID and sliding mode) using a 2-DOF vehicle model [ 7 ]. Similar methods were implemented by Andoni Medina et al who compared typical control methods used for ensuring vehicle stability and improving lap time for electric racing cars using PID and sliding controllers [ 11 ] as well as Leonardo De Novellis, et al who analyzed and compared different PID and sliding mode-based control techniques (e.g., SOSM controllers) [ 12 ]. Gökhan Tekin et al developed a fuzzy logic control scheme for active yaw rate and side slip angles feedback control [ 13 ].…”

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

“…Appendix A presents the control strategy math modeling in equations A1 to A26. This control methodology enables the IWM system to respond dynamically to changing conditions and adapt its torque output accordingly [29,30]. The Proportional-Integral (PI) control loop is a widely used control algorithm that combines two essential components: proportional control and integral control.…”

Sim-to-Real Application of Reinforcement Learning Agents for Autonomous, Real Vehicle Drifting

“…In [8], the authors address multiple scenarios where a maneuver at the handling limit significantly reduces fatality and collision risk. In addition to this reasoning, it is also an essential scope of AVC to enhance road vehicle transportation beyond human limits, especially considering the rapidly evolving field of overactuated vehicle control [9,10], which is an overly complex task for most human drivers. This makes this area of research not just important for safety-critical systems, but also for motorsports, space exploration, and military sciences.…”