Comprehensive performance of the vehicle, including ride qualities and road-holding, is essentially of great value in practice. Many up-to-date semi-active control algorithms improve vehicle dynamics performance effectively. However, it is hard to improve comprehensive performance for the conflict between ride qualities and road-holding around the second-order resonance. Hence, a new control algorithm is proposed to achieve a good trade-off between ride qualities and road-holding. In this paper, the properties of the invariant points are analysed, which gives an insight into the performance conflicting around the second-order resonance. Based on it, a new control algorithm is proposed. The algorithm employs a novel frequency selector to balance suspension ride and handling performance by adopting a medium damping around the second-order resonance. The results of this study show that the proposed control algorithm could improve the performance of ride qualities and suspension working space up to 18.3% and 8.2%, respectively, with little loss of road-holding compared to the passive suspension. Consequently, the comprehensive performance can be improved by 6.6%. Hence, the proposed algorithm is of great potential to be implemented in practice.

In-wheel motor-driven vehicles are the development trend for future vehicles due to its high energy efficiency and low emission as well as its flexibility to achieve independent steering, driving, etc. However, the weighted wheel of in-wheel electric vehicles involves more unexpected unsprung vibrations, which imposes adverse effect on vehicle ride comfort. In addition, there exists an invariant point around the unsprung resonance frequency in both controlled and uncontrolled suspensions, which greatly limits the elimination of unsprung adverse effect of in-wheel electric vehicles. In this paper, a combined structure is proposed to eliminate the unsprung adverse effect. The structure is composed of the vehicle suspension and a tuned mass damper, which are both controlled by a sliding mode controller, aiming at eliminating the unsprung adverse effect as well as improving ride comfort across the whole frequency spectrum. The tunes mass damper is used to get rid of the constraint of the invariant point. The simulation and hardware-in-the-loop results show that the root mean square of the sprung mass acceleration and tire deflection is reduced by 31.2% and 2.2% respectively, which indicates that the proposed method is effective and ride comfort is greatly improved.

Inverse optimal control (IOC) seeks to infer a control cost function that captures the underlying goals and preferences of expert demonstrations. While significant progress has been made in finite-horizon IOC, which focuses on learning control cost functions based on rollout trajectories rather than actual trajectories, the application of IOC to receding horizon control, also known as model predictive control (MPC), has been overlooked. MPC is more prevalent in practical settings and poses additional challenges for IOC learning since it is complicated to calculate the gradient of actual trajectories with respect to cost parameters. In light of this, we propose the inverse MPC (IMPC) method to identify the optimal cost function that effectively minimizes the discrepancy between the actual trajectory and its associated demonstration. To compute the gradient of actual trajectories with respect to cost parameters, we first establish two differential Pontryagin's maximum principle (PMP) conditions by differentiating the traditional PMP conditions with respect to cost parameters and initial states, respectively. We then formulate two auxiliary optimal control problems based on the derived differentiated PMP conditions, whose solutions can be directly used to determine the gradient for updating cost parameters. We validate the efficacy of the proposed method through experiments involving five simulation tasks and two real-world mobile robot control tasks. The results consistently demonstrate that IMPC outperforms existing finite-horizon IOC methods across all experiments.

Off-road environments with varied terrain and obstacle types present substantial challenges to the safe maneuvering of unmanned ground vehicles (UGVs). This study addresses the need for personalized path planning by introducing a multi-source off-road potential field (MOPF) method that quantifies risk and impediments in off-road settings based on internal and external characteristics. Specifically, Vehicle capability boundaries are defined by longitudinal dynamics analysis of the ego-vehicle to prevent instability due to insufficient driving force and limited adhesion conditions. A novel Non-Uniform Safety Margin Expression (NSME) is proposed to adjust the MOPF, allowing it to consider the vehicle’s state to enhance travel efficiency and minimize detours. The MOPF can be adapted according to the characteristics of the ego vehicle, drivers, and cargo. To incorporate driving styles, the Driving Style Probabilistic Roadmap (DSPRM) algorithm is developed, leading to smoother and more personalized paths. Comparative tests demonstrate that our method enables personalized path planning, achieving an average reduction of 10.29% in path length and 30.83% in path slope compared to traditional planning methods, while maintaining a safe distance from obstacles.