Coordinated Stability Control Strategy for Intelligent Electric Vehicles Using Vague Set Theory

Abstract

Aiming at improving the tracking stability performance for intelligent electric vehicles, a novel stability coordinated control strategy based on preview characteristics is proposed in this paper. Firstly, the traditional stability control target is introduced with the two degrees of freedom model, which is realized by the sliding mode control strategy. Secondly, an auxiliary control target further amending the former one with the innovation formulation of the preview characteristics is established. At last, a multiple purpose Vague set leverages the contribution of the traditional target and the auxiliary preview target in various vehicle states. The proposed coordinated control strategy is analyzed on the MATLAB/CarSim simulation platform and verified on an intelligent electric vehicle established with A&D5435 rapid prototyping experiment platform. Simulation and experimental results indicate that the proposed control strategy based on preview characteristics can effectively improve the tracking stability performance of intelligent electric vehicles. In the double lane change simulation, the peak value of sideslip angle, yaw rate, and lateral acceleration of the vehicle is reduced by 13.2%, 11.4%, and 8.9% compared with traditional control strategy. The average deviations between the experimental and simulation results of yaw rate, lateral acceleration, and steering wheel angle are less than 10% at different speeds, which demonstrates the consistency between the experimental and the simulation results.

The Eco-Driving Considering Coordinated Control Strategy for the Intelligent Electric Vehicles

“…Flow and transfer characteristics of energy flow is established as the physical models of motor and battery [13]. The change of vehicle states represented by kinematics and compliance has typical characteristics of substance flow and controlled by the vehicle dynamic control system [14]. The substance flow based evolution process of IEVs' movement focuses on the path-following and aims at a subtle control effect [15].…”

“…As the main function of IEVs, longitudinal automated driving framework consists of the adaptive cruise control in normal scenarios and obstacle avoidance in emergency scenarios [19]. Typical cruise control models for normal scenarios have been built considering car-following features and driving styles with comprehensive parameters [20]. In succession with the physical models, data-driven based adaptive cruise control models by integrating communication technologies have been proposed to achieve cooperative cruise functions [21].…”

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system

“…The research results in Li et al 12 and Han et al 13 all provided lateral control strategies; thus, a lateral motion control model was constructed. In the existing research presented by He et al and Lai and Huang, 5,6 the predictability of multiagent systems was studied. To bolster lateral control steadiness, the following control techniques have been formulated.…”

“…Gradually, classic control strategies such as sliding mode control, 3,4 integrated control, 5,6 model predictive control, 7 linear quadratic regulator (LQR) algorithms 8,9 and shared lateral controllers 10 have been developed in response to the varying control requirements imposed on autonomous vehicles.…”

A data‐driven chassis coordination control strategy

“…Various sensors and electronic systems equipped with intelligent vehicles can replace drivers to complete many complex driving tasks, and these systems are increasingly applied to the advanced chassis of manned or unmanned vehicles. This also means that the cooperative control of the chassis system becomes more and more important [4,5].…”