This paper proposes a novel integrated path following control scheme for a 4-Wheel Independent Drive (4WID) autonomous vehicle that can adaptively change its mechanism according to the driving conditions. The proposed integrated system handles the lateral steering controller, longitudinal speed, and yaw moment controls considering tire force capacity of each corner. For the lateral controller, the cornering stiffness uncertainties and the transient performance are considered and combined into an H∞ robust controller based on linear matrix inequality (LMI) theory. A super-twisting sliding mode controller (STSMC) based longitudinal controller is designed to deal with disturbances and suppress chattering. When encountering extreme conditions, the active yaw moment controller with hierarchical structure is adaptively activated to prevent large deviation from the reference path and maintain the stability of vehicle. For the tire force allocation, an optimization algorithm is proposed, which has flexible equality constraints to coordinate the longitudinal and lateral motions according to the driving conditions. Simulations based on Carsim-Simulink co-simulation platform show that the proposed method is effective and has excellent performance in both normal and extreme driving conditions.

针对紧急工况下车辆纵横向动力学耦合导致传统轨迹跟踪方法精度下降的问题,提出解耦循迹跟踪算法,该算法在利用动力学解耦消减耦合负效应的基础上,通过跟踪车辆目标运动状态实现运动轨迹的间接跟踪.首先,基于理论推导与仿真,探究了车辆纵横向动力学耦合成因及其对循迹跟踪精度的影响;然后,通过修正传统3自由度车辆动力学逆系统构型确定其接口,利用随机数据集训练反馈前向神经网络(BPNN)模型以获取车辆平面运动逆系统;最后,设计基于目标运动轨迹的目标运动状态逆解算模型与基于纯跟踪思想的目标轨迹修正模型,将逆系统解耦方法应用于长航程循迹跟踪任务中.仿真与实验结果说明解耦循迹跟踪作为一种全新的跟踪方法,不仅可以完成跟踪任务,且通过与传统循迹跟踪方法对比,在耦合工况下所提出的方法具备更高的跟踪精度.

Trajectory tracking and stability control are two essential functions of autonomous vehicles, and there is inevitable mutual interference between them, especially under extreme conditions. Towards addressing this challenge, this paper proposes a novel cooperative strategy of trajectory tracking and stability control for four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed based on the model predictive control (MPC) theory, in which the tire cornering stiffness is modified in real-time leverages the lateral force estimated by the square-root cubature Kalman filter (SCKF). Then, the vehicle stability controller is designed based on the sliding mode control (SMC), in which the tire force saturation constraint and the relative weight of yaw rate and sideslip angle are considered. Furthermore, a weight adaptive criterion with normalized stability index is defined to develop the cooperative strategy of trajectory tracking and stability control. The feasibility and adaptability of the proposed method to different extreme conditions are verified by hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. Finally, an experimental case is presented to demonstrate the realizability of the proposed method.

For in-wheel driving vehicle electric vehicles (EVs), mechanical electromagnetic coupling effect caused by the air gap deformation in permanent magnet synchronous hub motor and intensified by the road excitation deteriorates the EVs performance. In this paper, after studying the numerical method for multi-field coupling problems of hub-driving vehicle under the coupled action of electromagnetic field and mechanical field. The experimental validation is investigated. The results indicate that the multi-field coupling effect in hub-driving motor worsens the dynamics performance of the vehicle. To enhance the vehicle performance, suppress mechanical electromagnetic coupling effect and, at same time, reduce the influence of controllable suspension time delay, a delay-dependent H∞ controller is designed based on Lyapunov theory. By applying the particle swarm optimization (PSO) algorithm and the linear matrix inequality theory, the desired output controller gain is derived. Numerical simulations reflect that the active suspension controller considering control time delay not only achieves the favorable riding comfort performance and restrains the coupling effect in hub driving motor but also ensures the suspension deflection and the safety performance requirement. Moreover, it maintains the closed-loop asymptotically stability regardless of t the variation on the sprung mass and control time delay.

In the research of autonomous vehicles, most existing studies treat the decision/planning and control as two separate problems. This idea originates from robotics. But since there are essential differences between robot and autonomous vehicle, the structure in Robotics may not be suitable for autonomous vehicles. Considering decision/planning and control separately may affect the performance of autonomous vehicle under complex driving conditions. To fill in the research gap, this paper proposes a novel scheme which considers the local motion planning and control in a combined manner. Firstly, the local motion planning is transformed into the longitudinal control problem based on the proposed scenario adaptive MPC, by which the motion behavior (driving along the global path, car-following, lane-change) can be automatically decided. Then, the lateral MPC controller is designed to track the global path and conduct the local motion commands. To ensure the performance of the path tracking control and a smooth lane-change process simultaneously, an adaptive weight mechanism is introduced in the lateral controller. Comprehensive case studies including both straight and curve road are conducted based on Carsim-Simulink co-simulation platform. The results show that the proposed algorithm can not only ensure the vehicle safety in complex driving conditions, but also ensure that the vehicle can drive at its desired velocity as much as possible by intelligently judging the most proper motion behaviors.

In order to identify the road friction coefficient of the left and right sides of the vehicle more accurately, this paper presents an estimation framework based on a novel tyre model and modified square-root cubature Kalman filter (SCKF). To begin with, a novel tyre model is proposed by adaptively calculating the longitudinal and lateral stiffness and the effective friction coefficient. The tyre forces calculated by this model are more accurate than those calculated by the Brush model. Then, to avoid the influence of abnormal measurement noise on the estimation effect, we develop an improved SCKF (ISCKF) algorithm based on the maximum correntropy criterion. The algorithm can update the measurement noise covariance adaptively. Furthermore, a real-time estimation scheme of road friction coefficient is designed by combining the vehicle dynamics model with the proposed novel tyre model and ISCKF algorithm. Finally, the performance of the presented method is verified by the co-simulation of CarSim and MATLAB/Simulink. The results show that the designed estimation scheme not only has excellent robustness in the case of abnormal measurement noise interference but also has good adaptability to the uncertainty of road friction coefficients distribution.

Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

For a state estimation problem of nonlinear system, the square-root cubature Kalman filter (SCKF) is an effective method when the noise statistical characteristics are known. However, the performance of SCKF often degrade significantly in the face of uncertain noises interference, particularly in case of measurement or system failure. In this paper, we focus on improving the accuracy and robustness of SCKF under irregular noise. First, a weighted adaptive SCKF (WASCKF) algorithm is presented with moving window method. The WASCKF can improve the accuracy of SCKF by adaptively adjusting the covariances of measurement noise and process noise. Next, in order to further improve the robustness of WASCKF against the abrupt abnormal noise, a correction adaptive SCKF (CASCKF) algorithm based on fault detection mechanism is proposed. The CASCKF algorithm can detect whether there is a fault according to a statistical function of Chi-square distribution, and can judge and carry out the necessary correction processing by using an isolate rule. Finally, the performance of CASCKF is verified by numerical experiments of autonomous vehicle target tracking problem. The results show that the proposed CASCKF algorithm has good accuracy and robustness even with sudden abnormal noise interference.

This paper proposes a novel motion planning and tracking framework based on improved artificial potential fields (APFs) and a lane change strategy to enhance the performance of the active collision avoidance systems of autonomous vehicles on structured roads. First, an improved APF-based hazard evaluation module, which is inspired by discrete optimization, is established to describe driving hazards in the Frenet-Serret coordinate. Next, a strategy for changing lane is developed in accordance with the characteristics of the gradient descent method (GDM). On the basis of the potential energy distribution of the target obstacle and road boundaries, GDM is utilized to generate the path for changing lane. In consideration of the safety threats of traffic participants, the effects of other obstacles on safety are taken as additional safety constraints when the lane-changing speed profile for ego vehicles is designed. Then, after being mapped into the Cartesian coordinate, the feasible trajectory is sent to the tracking layer, where a proportional-integral control and model predictive control (PI-MPC) based coordinated controller is applied. Lastly, several cases composed of different road geometrics and obstacles are tested to validate the effectiveness of the proposed algorithm. Results illustrate that the proposed algorithm can achieve active collision avoidance in complex traffic scenarios.

为提高分布式驱动电动智能汽车在自主循迹过程中关键参数的估计精度并降低模型不确定性对控制系统鲁棒性的影响,本文中提出了一种基于观测器的自适应滑模路径跟踪控制策略.首先,针对难以直接精确测量的车辆纵、侧向速度,建立了5输入3输出3状态的状态估计系统,并采用最小模型误差准则以降低估计过程轮胎的非线性特性带来的观测模型误差.接着,基于运动学模型,计算出了路径跟踪期望横摆角速度响应,并采用自适应滑模算法实现主动转向控制.考虑线控转向系统的潜在失效风险,引入径向基神经网络对系统不确定性进行在线估计.同时,设计了直接横摆稳定控制器并采用最优转矩分配策略,进一步提高车辆的稳定性.最后,对车辆状态估计和路径跟踪进行了Carsim/Matlab联合仿真,结果表明:基于最小模型误差准则的观测器能取得较可靠的估计结果,路径跟踪控制器能保证车辆具有较好的跟踪精度和鲁棒性.

： In order to improve the path following performance of intelligent vehicles while ensure their dynamics stability in extreme conditions, control and coordination algorisms are designed based on the respective characteristics of steering and direct yaw moment (DYC) systems for four-wheel drive electric intelligent vehicles. Firstly, for the uncertainties of tire cornering stiffness during steering maneuvers, a robust controller is proposed based on linear matrix inequality (LMI) theory, which also has the ability to realize the regional pole assignment. And the solution of the controller is also investigated. For the DYC system, a hierarchical structure is proposed; a linear-time-varying model predictive control (LTV-MPC) method is utilized to generate the desired yaw rate in the upper-level controller based on the kinematics relationship between vehicle and road; the lower level controller obtains the active yaw moment by hyperbolic-tangent based sliding mode controller, and to ensure the stability of vehicle, the side slip angle is considered in the sliding surface, with its weight decided by side slip phase plane index. Considering that in most situations, the steering system alone can achieve satisfactory performance, an activate mechanism is introduced for DYC. Under the mechanism, DYC will not be involved until the steering system is judged unable to complete the control task, this can prevent the energy lost caused by most unnecessary involvement of DYC. Finally, results based on Simulink-CarSim co-simulation shows that the proposed controller can still have satisfactory path following performance even under relatively extreme conditions, while the dynamics stability is well maintained.

Path-following control is one of the key technologies of autonomous vehicles, but the complex coupling effects and system uncertainties of vehicles can degrade their control performance. Accordingly, this study proposes targeted methods to solve different types of coupling in vehicle dynamics. First, the types of coupling are figured out and different handling strategies are proposed for each type, among which the coupling caused by steering angle, unsaturated tire forces, and load transfer can be treated as uncertainties in a unified form, such that the coupling effects can be treated in a decoupling way. Then, robust control methods for both lateral and longitudinal dynamics are proposed to deal with the uncertainties in dynamic and physical parameters. In lateral control, a robust feedback–feedforward scheme is utilized in lateral control to deal with such uncertainties. In longitudinal control, a radial basis function neural network-based adaptive sliding mode controller is introduced to deal with uncertainties and disturbances. In addition, the tire saturation coupling that cannot be handled by controllers is treated by a proposed speed profile. Simulation results based on the CarSim–Simulink joint platform evaluate the effectiveness and robustness of the proposed control method. The results show that compared with a well-designed robust controller, the velocity tracking performance, lateral tracking performance, and heading tracking performance improve by 55.68%, 34.26%, and 52.41%, respectively, in the double-lane change maneuver, and increase by 87.79%, 30.18%, and 9.68%, respectively, in the ramp maneuver.

This paper presents a robust output-feedback guaranteed-cost control strategy for the path following control of autonomous vehicles. First, the model of vehicle dynamics and path following is established, which takes the uncertainties of cornering stiffness into account. Then, to deal with such uncertainties and improve the transient performance, a robust guaranteed-cost controller is introduced with the regional pole constraint ability. Considering that it is expensive and difficult to measure the side slip angle accurately, the proposed controller utilises an output-feedback scheme without side slip angle information. Moreover, the particle swarm optimisation (PSO) algorithm is selected to optimise the performance index of the guaranteed-cost controller such that the priorities among different objectives can be decided reasonably. Simulation results demonstrate the effectiveness of the proposed controller and its advantages over previous studies in the presence of parameter uncertainties.

The path-following problem for four-wheel independent driving and four-wheel independent steering electric autonomous vehicles is investigated in this paper. Owing to the over-actuated characters of four-wheel independent driving and four-wheel independent steering autonomous vehicles, a novel yaw rate tracking-based path-following controller is proposed. First, according to the kinematic relationships between vehicle and the reference path, the yaw rate generator is designed by linear matrix inequality theory, with the ability to minimize the disturbances caused by vehicle side slip and varying curvature of path. Considering that the path-following objective and dynamics stability are in conflict with each other in some extreme path-following conditions, a coordinating mechanism based on yaw rate prediction is proposed to satisfy the two conflicting objectives. Then, according to the desired yaw rate and longitudinal velocity, a hierarchical structure is introduced for motion control. The upper-level controller calculates the generalized tracking forces while the allocation layer optimally distributes the generalized forces to tires considering tire vertical load and adhesive utilization. Finally, simulation results indicate that the proposed method can achieve excellent path-following performances in different driving conditions, while both path-following objective and dynamics stability can be satisfied.

With the electrification and intellectualization of vehicle systems, electromagnetic active suspension has been paid more and more attention. Linear motor is one of the effective actuators of the electromagnetic active suspension system. The nonlinear factors of linear motor, such as nonlinear friction force and ripple force, as well as power limit and magnetic saturation, will reduce the performance of electromagnetic active suspension. However, the current research rarely considers the effect of these nonlinear factors on active suspension control. In this article, the effect of nonlinearities of linear motors on electromagnetic active suspension performance and the ways to improve their performance are studied. An adaptive filtering compensation method is proposed to reduce the influence of nonlinear factors on the electromagnetic active suspension control. According to the simulated calculations, performance degradation of the active suspension is observed in both the primary control objective and high-frequency range due to inherent disturbance from the nonlinear factors. Also, the electromagnetic nonlinearities will reduce the active suspension effective force output. By proposing an adaptive compensator based on the filtered-x recursive least squares algorithm, the first-order resonance of the suspension system could be controlled and the electromagnetic active suspension effective force could be magnified. Also, convergence of the adaptive compensator is found to be rapid and reasonable.

A simultaneous trajectory tracking and stability control method is present for the four-wheel independent drive (4WID) automated vehicles to handle dynamic coupling maneuvers. To conquer the disadvantage that attendant disturbances caused by the dynamic coupling of traditional decentralized control methods degenerate the trajectory tracking accuracy, the proposed method takes advantage of the idea of decoupling to optimize the tracking performance. After establishing the dynamic model of the 4WID automated vehicles, the coupling mechanism of the vehicle dynamic control and its negative effect on trajectory tracking were studied at first. The inverse system model was then determined by machine learning and connected in series with the controlled object to form a pseudo linear system to realize dynamic decoupling. Finally, differing from previous tracking methods following the apparent lateral position and longitudinal velocity references, the pseudo linear system tracks the ideal intermediate targets transferred from the target trajectory, that is, the accelerations of vehicle in longitudinal, lateral and yaw directions, to indirectly achieve trajectory tracking and validly restrain the vehicle motion. The effectiveness of the proposed method, i.e., the high tracking accuracy and the stable driving performance, is verified through three coupling driving scenarios in the CarSim-Simulink co-simulations platform.

Aiming at the difficulty of vehicle lateral control in high-speed condition, a comprehensive control method that integrates active steering (AS) and direct yaw moment control (DYC) systems is proposed for four-wheel independent driving and four-wheel independent steering (4WID/4WIS) vehicle. According to the characteristics of lateral dynamics in high-speed conditions, different principles are adopted in different steering conditions. In a slight or moderate steering, a decoupling control method is proposed, and a penalty function is used to allocate the involvement of AS and DYC for the over-actuated characteristics of 4WID/4WIS vehicle. While in a large steering, based on sideslip phase plane, handling-oriented and stability-oriented control are proposed in order to realise a fine trade-off between handling and stability performance. A transient layer is introduced in the phase plane to further improve the performance of this controller. And the allocation of active yaw moment is also discussed. Simulation results demonstrate that the proposed integrated control method can effectively improve the lateral dynamics performance of the vehicle in high-speed condition as compared to previous control methods.

This paper presents a novel adaptive multiple-model predictive control (MPC) scheme for Four-Wheel Independent Drive (4WID) autonomous vehicles with a holistic structure. Firstly, the combined vehicle-path model is established. To ensure the real-time performance of MPC, the coupling relationships in the control output and the longitudinal/lateral motions are well decoupled, in this way a linear integrated model is utilized as the internal model of the controller. Then, the holistic MPC is proposed to acquire the steering angle and the force on each corner. Based on the advantages of the proposed structure, a weight adaptive mechanism is introduced to improve the handling ability of the controller to various driving conditions, especially some extreme conditions. For the uncertainties in tire cornering stiffness, a multiple-model adaptive law is designed with its convergence proved by Lyapunov theory. Numerical results based on Carsim-Simulink co-simulation platform demonstrate the effectiveness and superiority of the proposed control method in both normal and extreme driving conditions.

Aiming at the coupling phenomenon of longitudinal and lateral motions for automobile, autonomous vehicle with Four-wheel-driving and front-wheel-steering was set as the subject investigated. A dynamic model which reflects the longitudinal and lateral motion of vehicle was established and the reversibility of this model was analyzed by the interactor algorithm. On the basis of the existing classical structure of pseudo-linear system, the pseudo linear composition system with the ability to fit the upper level planning system of intelligent vehicle was established according to the characteristic of intelligent vehicle. In order to realize the decoupling of longitudinal and lateral motions for vehicle, an approach based on network inverse method was proposed as the decoupling control strategy in this paper, which can be combined with the internal model controller to form closed loop structure and it can significantly improve the performance of the plant by feedback and adjust the longitudinal speed and yaw rate of automobile. The simulation results validated the decoupling performance of the proposed approach. The results also showed that when compared with other control algorithms, the proposed approach can achieve good tracking performance of longitudinal speed and yaw rate under varieties of input condition. Further, the sideslip was constrained in a small range, which is beneficial to the path tracing accuracy and the stability of autonomous vehicle. Keywords: intelligent vehicle, decoupling control of longitudinal and lateral motion, neural networks, inverse approach, vehicle dynamics