The Development of a Brake Based Torque Vectoring System for a Sport Vehicle Performance Improvement

On Torque Vectoring Control: Review and Comparison of State-of-the-Art Approaches

“…Notably, this method does not account for tire-road friction, which physically restricts the maximum attainable yaw rate. To address this limitation, a more sophisticated approach is introduced, allowing for a detailed design of the reference understeer characteristic [6,24,28,[43][44][45][46][47][48][49][78][79][80][81][82]. In relation to that, a piecewise function defines the relationship between the dynamic steering angle δ dyn and the lateral acceleration a y , which, under quasi-steady-state conditions, is directly associated with the reference yaw rate ( .…”

“…Since PID is a feedback control, many authors have suggested adding a feedforward contribution to allow a quicker system response. Indeed, while the feedback aims to guarantee robust stability and disturbance rejection, the feedforward contribution allows a faster system response [25,28,29,43,45,46,97]. For instance, the authors in [45] propose a control logic for vehicle lateral dynamics consisting of a feedforward and a feedback contribution, as depicted in Figure 6.…”

“…A similar approach to that presented in [28] is proposed, except for rear tire slips, which is controlled in feedback, attempting to maximize the longitudinal force at the axle using a PI controller. The main focus of the article is on torque distribution, attempting to keep the torque on the rear axle at a level saturating the tires, with the remaining required amount sent to the front axle through the actuation of a center clutch.…”

“…TV potential in enhancing vehicle handling is so significant [18,19] that these control strategies were adopted before the advent of electric motors. At that time, TV was either actuated through active and semi-active differentials [20][21][22][23][24][25] or through individually controlled wheel brakes [21,[26][27][28][29][30], as it is still used nowadays in some applications [31,32]. Nevertheless, both actuation systems are less effective than electric motors because of slower dynamics, lower efficiency and flexibility, while also showing an increased integration complexity.…”

“…For this reason, the authors in [42] proposed a correction factor based on a feedforward neural network adopting the steering angle and the vehicle speed as inputs. Later, a more advanced approach was proposed in [24] and adopted by many other authors in a slightly modified formulation [6,28,[43][44][45][46][47][48] where the idea is to fully design the understeer characteristics of the vehicle with any shape not limited to the linear behavior. Using this approach, different vehicle behaviors can be obtained, corresponding to different understeer characteristics, being denoted as the driving modes [45,49].…”