This paper investigates the application of contusion stability yaw control of a lightweight solar-electric vehicle. The vehicle’s customized design envelope makes it more sensitive to variations in load due to its low weight and relatively large size. To address this issue, control strategies were developed using differential motor torques to generate direct yaw moments using the vehicle’s rear in-wheel motors. This paper introduces the working conditions of solar vehicles and demonstrates the necessity of stability control. Vehicle parameters such as mass and center of gravity position are obtained to apply control to the real vehicle. The paper then describes two stability control strategies, using (i) sliding-mode control (SMC) and (ii) model predictive control (MPC). To account for the road bank angle of the test area and the impact of additional weight from a driver and passenger, a Kinematic-Based Observer is designed to estimate the vehicle’s side-slip based on measured values. To collect real-time data, a dSPACE MicroAutobox was installed on the solar vehicle. The results show the effect of the observer and controllers under different vehicle speeds and load conditions. Finally, closed-loop simulation results are presented to support the findings from the open-loop testing.

Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance.

Feedback is an integral part of the learning process. However, delivering feedback effectively remains challenging, particularly within massified higher education systems that are characterised by large cohorts and increasingly diverse student populations. The emergence of generative artificial intelligence (GenAI) enables new ways of embedding feedback into educational offerings, some of which may be highly beneficial. In this paper, we introduce Abundant Feedback as a conceptual lens for examining the new capabilities that may be enabled by GenAI. We present a four-dimensional framework identifying the dimensions of GenAI feedback as abundance of Volume, of Availability, of Relevance and of Character. Through a systematic literature search, we describe how these dimensions manifest in recent empirical studies, and identify two educational domains, Computer Programming and Foreign Languages, as early adopters of AI-generated feedback. Beyond merely digitising existing scarce feedback processes, we discuss the emergence of new learner-driven feedback practices that are enabled by abundance, that both stimulate and demand student feedback literacy. Our multi-dimension abundance framework provides a lens, as well as the vocabulary and conceptual tools, to guide the implementation of GenAI feedback in ways that help realise the potential of artificial intelligence to enhance student learning.