Coordinated path-following and direct yaw-moment control of autonomous electric vehicles with sideslip angle estimation

Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.

An Adaptive Hierarchical Trajectory Following Control Approach of Autonomous Four-Wheel Independent Drive Electric Vehicles

In this article, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles with consideration of modeling errors and complex driving scenarios. In order to guarantee operational safety and robustness, a supertwisting second-order sliding mode (SOSM) control strategy is employed to suppress the heavy chattering issue existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy, which combines the SMC algorithm and NDOB technique is proposed based on stability analysis of the corresponding closed-loop system. Finally, hardware-in-the-loop experimental results show that the supertwisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering phenomenon.

Path Following Control of Autonomous Four-Wheel-Independent-Drive Electric Vehicles via Second-Order Sliding Mode and Nonlinear Disturbance Observer Techniques

This paper discusses dynamic control design for automated driving of vision-based autonomous vehicles, with a special focus on the coordinated steering and braking control in emergency obstacle avoidance. An autonomous vehicle is a complex multi-input and multi-output (MIMO) system, which possesses the features of parameter uncertainties and strong nonlinearities, and the coupled phenomena of longitudinal and lateral dynamics are evident in a combined cornering and braking maneuver. In this work, an effective coordinated control system for automated driving is proposed to deal with these coupled and nonlinear features and reject the disturbances. First, a vision algorithm is constructed to detect the reference path and provide the local location information between vehicles and reference path in real time. Then, a novel coordinated steering and braking control strategy is proposed based on the nonlinear backstepping control theory and the adaptive fuzzy sliding-mode control technique, and the asymptotic convergence of the proposed coordinated control system is proven by the Lyapunov theory. Finally, experimental tests manifest that the proposed control strategy possesses favorable tracking performance and enhances the riding comfort and stability of autonomous vehicles.

Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

In this paper, an efficient strategy is proposed to design the automatic steering controller for trajectory tracking of unmanned vehicles, which is robust with respect to the inherent nonlinearities and uncertainties of vehicles. The proposed automatic steering controller consists of a feedback part and a feedforward part. First, a fuzzy controller is proposed as the feedback part, and the parameters of membership functions and rules are optimized by genetic algorithms (GAs) to guarantee high performance. Then, a feedforward controller is designed to assist the controller when the vehicle is engaged in a curved section of trajectory, which utilizes preview information regarding upcoming curvature of reference trajectory to calculate a preview steering angle so that it offsets the disturbance of curvature. Both simulation and experimental results show that the proposed strategy can robustly track the reference trajectories under various conditions with high accuracy.

Design of Automatic Steering Controller for Trajectory Tracking of Unmanned Vehicles Using Genetic Algorithms

Lateral control is considered to be one of the toughest challenges in the development of automated vehicles due to their features of nonlinearities, parametric uncertainties and external disturbances. In order to overcome these difficulties, an adaptive fuzzy-sliding mode control strategy used for lateral control of vision-based automated vehicles is proposed in this paper. First, a vision algorithm is designed to provide accurate location information of vehicle relative to reference path. Then, an adaptive fuzzy-sliding mode lateral controller is proposed to counteract parametric uncertainties and strong nonlinearities, and the asymptotic stability of the closed-loop lateral control system is proven by the Lyapunov theory. Finally, experimental results indicate that the proposed algorithm can achieve favourable tracking performance, and it has strong robustness.

An adaptive fuzzy-sliding lateral control strategy of automated vehicles based on vision navigation

This paper describes the development of a robust longitudinal controller for autonomous ground vehicle with inherent unknown nonlinearities and parametric uncertainties. The longitudinal controller is designed using Sliding-Mode Control (SMC) strategy based on fuzzy logic, which works through switching between the throttle actuator and brake actuator according to a predetermined criterion. The proposed longitudinal controller not only eliminates the chattering phenomenon in the Sliding-Mode Control (SMC) but also copes with the system uncertainties and external disturbances. Additionally, the convergence of closed-loop longitudinal control system is proved by the Lyapunov stability theory. Finally, simulation and experimental results indicate the strong robustness and commendable tracking performance of proposed controller.

A robust longitudinal sliding-mode controller design for autonomous ground vehicle based on fuzzy logic

Since the vehicle longitudinal dynamic system inherently has non-linear and time-varying behaviour, it is a challenging work to design an effective automatic speed control strategy to handle the unknown nonlinearities and uncertain parameters. In this paper, a novel adaptive fuzzy sliding automatic speed controller with variable boundary layer for intelligent vehicles is proposed; the control gains and the boundary layer thicknesses of the sliding mode speed controller are adaptively tuned by fuzzy logic simultaneously. Additionally, the stability of the proposed closed-loop automatic speed control system is guaranteed by the Lyapunov theory, and the switching criterion between the throttle and brake actuators is introduced. Finally, the performance of the proposed control scheme is evaluated by simulation and experimental tests, and the results illustrate that the proposed scheme can achieve good tracking performance.

Rongben Wang

Papers

Design of Automatic Steering Controller for Trajectory Tracking of Unmanned Vehicles Using Genetic Algorithms

An adaptive fuzzy-sliding lateral control strategy of automated vehicles based on vision navigation

A robust longitudinal sliding-mode controller design for autonomous ground vehicle based on fuzzy logic

A novel fuzzy-sliding automatic speed control of intelligent vehicles with adaptive boundary layer