Vehicle dynamics

About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.

Lateral Vehicle Trajectory Optimization Using Constrained Linear Time-Varying MPC

Abstract: In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral vehicle guidance task along a reference curve as a constrained optimal control problem. The optimization problem is solved by means of a linear time-varying model predictive control scheme that generates trajectories for path following under consideration of various time-varying system constraints in a receding horizon fashion. Formulating the system dynamics linearly in combination with a quadratic cost function has two great advantages. First, the system constraints can be set up not only to achieve collision avoidance with both static and dynamic obstacles, but also aspects of human driving behavior can be considered. Second, the optimization problem can be solved very efficiently, such that the algorithm can be run with little computational effort. In addition, due to an elaborate problem formulation, reference curves with discontinuous, high curvatures will be effortlessly smoothed out by the algorithm. This makes the proposed algorithm applicable to different traffic scenarios, such as parking or highway driving. Experimental results are presented for different real-world scenarios to demonstrate the algorithm’s abilities.

Virtual sensor: application to vehicle sideslip angle and transversal forces

Abstract: This paper compares four observers (virtual sensors) of vehicle sideslip angle and lateral forces. The first is linear and uses a linear vehicle model. The remaining observers use an extended nonlinear model. The three nonlinear observers are: extended Luenberger observer, extended Kalman filter and sliding-mode observer. Modeling, model simplification, and observers are described, and an observability analysis is performed for the entire vehicle trajectory. The paper also deals with three different sets of sensors to see the impact of observers results. Comparison is first done by simulation on a valid vehicle simulator, and then observers are used on experimental data. Our study shows that observers are more accurate than simple models as regards unmeasurable variables such as sideslip angle and transversal forces. It also shows that speed of center of gravity is not an indispensable variable here.

Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults

Abstract: This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial–parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.

Innovation by in-wheel-motor drive unit

Abstract: The in-wheel-motor (IWM) will be the most important key technology in the near future to be used by electric vehicles (including fuel cell vehicles). In the past 100 years of the internal combustion engine, several kinds of vehicle packages have been developed, for example, front-engine front-wheel drive, front-engine rear-wheel drive, mid-engine rear-wheel drive, and rear-engine rear-wheel drive. However, a conclusive solution for the best package has not been found. Combining electric drive and IWM enables both good vehicle dynamics and a roomy interior. In addition, the responsiveness of IWM raises the performance of the dynamic control to an even higher level.

Cooperative Distributed Robust Trajectory Optimization Using Receding Horizon MILP

Abstract: Motivated by recent research on cooperative unmanned aerial vehicles (UAVs), this paper introduces a new cooperative distributed trajectory optimization approach for systems with independent dynamics but coupled objectives and hard constraints. The overall goal is to develop a distributed approach that solves small subproblems while minimizing a fleet-level objective. In the new algorithm, vehicles solve their subproblems in sequence while generating feasible modifications to the prediction of other vehicles' plans. In order to avoid reproducing the global optimization, the decisions of other vehicles are parameterized using a much smaller number of variables than in the centralized formulation. This reduced number of variables is sufficient to improve the cooperation between vehicles without significantly increasing the computational effort involved. The resulting algorithm is shown to be robustly feasible under the action of unknown but bounded disturbances. Furthermore, the fleet objective function is proven to monotonically decrease as the algorithm cycles through the vehicles in the fleet and over the time. The results from simulations and a hardware experiment demonstrate that the proposed algorithm can improve the fleet objective by temporarily having one vehicle sacrifice its individual objective, showing the cooperative behavior.