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This paper proposes an integrated vehicle dynamics control system that aims to enhance vehicles handling stability and safety performance by coordinating active front steering (AFS) and active suspension systems (ASS). The integrated controller design is based on the lateral stability region described by phase plane approach that is employed to bound the vehicle stability and coordinate AFS and ASS. During normal steering conditions, the vehicle states lie inside the lateral stability region, only the AFS is involved for vehicle steerability enhancement. Whereas, when the vehicle reaches the handling limits and the vehicle states go outside the lateral stability region under extreme steering maneuvers, both AFS and ASS collaborate together to improve vehicle handling stability. The linear parameter-varying (LPV) polytopic vehicle model is built, which depends affinely on the time-varying longitudinal speed that is described by a polytope with finite vertices. The resulting gain-scheduling state-feedback controller is designed and solved utilizing a set of linear matrix inequalities derived from quadratic H∞ performance. Simulation using matlab/simulink-carsim® is carried out to evaluate the performance of the integrated controller. The simulation results show the effectiveness of the proposed controller.

In addition to the longitudinal dynamics, the lateral control of the platoon can significantly affect its performance on winding road. This paper presents a platoon control framework on winding road for electric vehicles subject to stochastic communication delay and interference. The intervehicle spacing errors (ISEs) in both longitudinal and lateral directions are transformed to an arc-length-based form first. Then, the relationship between single vehicle dynamics and the ISEs is created based on the feedback linearization of the nonlinear system and the arc-length parametric representation of the directed curve. In this way, the whole platoon can be represented by three decoupled linear single-input and single-output systems, i.e., the longitudinal, lateral, and yaw. To assure the steady-state stability of the platoon on a winding road, a robust controller based on the H∞ method is designed to suppress the affection of the communication delay and interference. Also, sufficient conditions that achieve the transient stability of the platoon are derived. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the proposed platoon control can realize the stability of the platoon as well as the supernal road traceability.

This paper introduces the development of an autonomous driving system in autonomous electric vehicles, which consists of a simplified motion-planning program and a Model-Predictive-Control-Based (MPC-based) control system. The motion-planning system is based on polynomial parameterization, which computes a path toward the expected longitudinal and lateral positions within required time interval in real scenarios. Then the MPC-based control system cooperates the front steering and individual wheel torques to track the planned trajectories, while fulfilling the physical constraints of actuators. The proposed system is evaluated through simulation, using a seven-degrees-offreedom vehicle model with a ‘magic formula’ tire model. The simulations and validation through CarSim show that the proposed planner algorithm and controller are feasible and can achieve requirements of autonomous driving in normal scenarios.

Purpose -The purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles. Design/methodology/approach -An optimization algorithm, model predictive control (MPC) and Karush-Kuhn-Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted. Findings -The effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption. Originality/value -This paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path. Stable trajectory planningLiwei Xu et al.