## 1 Introduction
[15] proposed a novel direct yaw moment control system with cascaded traction and yaw stability control. The experimental results show that the proposed control system can adapt to the road condition and attenuate the yaw rate error. [PERSON] et al. [11] developed a feed-forward type of tire force distribution control together with motion control of a full drive-by-wire electric vehicle to improve vehicle stability and reduce the tire energy dissipation caused by tire slip. More recently, [PERSON] [4] and [PERSON] [16] studied the potential of electric single wheel drive to compensate for rear wheel drive with high rear axle load, and they also developed a prototype vehicle with a TV control concept with the main aim to show the potential of single wheel drive in general, but demonstrated that it is indeed possible to reshape the vehicle handling both in terms of steady-state understeering characteristics and transient response using TV control [17; 18]. In a related study by [PERSON] et al. [19], the simulation results show the robustness of the same control concept. Lightweight vehicles and large variation in loading condition were not considered in earlier research [4; 16; 17; 18; 19], but the results illustrate the potential of using DYC for this application. [PERSON] [1; 20] developed a TV feed-forward control system with an online model parameter identification approach to reduce the load sensitivity of a lightweight vehicle in terms of the overall vehicle dynamics. Model-based control normally relies on nominal values for the internal model parameters. As stated above, for vehicle-dynamic control of lightweight vehicles the actual vehicle and tire parameters differ on a wide range between individual trips. Therefore, the nominal values should be updated for each trip to be valid. [PERSON] [1] determined valid vehicle and tire parameters for each trip using a driving synchronous estimation method and verified the performance in multibody dynamics simulations. The developed estimation algorithm is able to estimate vehicle and tire parameters but requires minutes of journey time. The potential of using such an online model identification approach together with TV control was also presented for trip optimal feed-forward control, but neither the details regarding the TV control design nor results with the combined control and estimation design were included in the publications by [PERSON] [1, 20]. Furthermore, the performance of the closed-loop driver-vehicle system with TV control is not evaluated in earlier research [4, 16, 17, 18, 19, 20]. Based on the abovementioned overview, the DYC system proposed in this paper is based on feed-forward of front steering angular velocity and a yaw rate feedback with the aim to reduce the sensitivity of the vehicle handling dynamics to the loading condition. More specifically, supported by the results of the previous study [7], the control objective of the proposed DYC system is not only the overall vehicle dynamics [18, 20], but instead the objective is more focused on the transient dynamics. The feed-forward controller is derived based on model following control with approximation of the vehicle dynamics to 1 st-order transfer function. The target electric vehicle with in-wheel motors generate the demanded yaw moment by transverse distribution of traction forces [11, 14, 21, 22, 23, 24]. The current work is an extension of the previous study [7], with the aim to make the determination of the yaw rate feedback gain model-based and adaptable to various vehicle velocity conditions and, therefore, the yaw rate feedback gain in the DYC system is determined in a way that the steady-state yaw rate gain of the controlled vehicle matches the one in the case of unloaded vehicle. The aim is also to evaluate and verify the effectiveness of the proposed DYC system based on simulations and an experimental study using a fix-based driving simulator with a human driver in the loop. This paper is organized as follows.