Integrated vehicle control through the coordination of longitudinal/lateral and vertical dynamics controllers: Flatness and LPV/‐based design

This paper deals with global chassis control of automotive vehicles. It focuses on the coordination of suspension and steering/braking vehicle controllers based on the interaction between the vertical and lateral behaviors of the vehicle. It is shown that the lateral acceleration and resulting roll motion of the car generate load transfers that considerably affect vehicle stability. A control law is designed in hierarchical way to improve the overall dynamics of the vehicle and cope with coupled driving maneuvers like obstacle avoidance using steering control and stop‐and‐go control using braking or driving wheel torque. This global control strategy includes two types of controllers. The first one is the longitudinal/lateral nonlinear flatness controller. Based on an appropriate choice of flat outputs, the flatness proof of a 3 DOF two‐wheel nonlinear vehicle model is established. Then, the combined longitudinal and lateral vehicle control is designed using algebraic estimation techniques to provide an accurate estimation of the derivatives and filtering of the reference flat outputs. The second part of the proposed strategy consists of a linear parameter‐varying/ suspension controller. This controller uses lateral acceleration as a varying parameter to account for load transfers that directly affect the suspension system. The coordination between the vehicle vertical and lateral dynamics is highlighted in this study, and the linear parameter‐varying/ framework ensures a specific collaborative coordination between the suspension and the steering/braking controllers, to achieve the desired performance. Simulations on a complex full vehicle model have been validated using experimental data obtained on‐board a real Renault Mégane Coupé. Copyright © 2017 John Wiley & Sons, Ltd.     

Coupled nonlinear vehicle control: Flatness-based setting with algebraic estimation techniques

A combined nonlinear longitudinal and lateral vehicle control is investigated. Flatness-based nonlinear control and new algebraic estimation techniques for noise removal and numerical differentiation are the main theoretical tools. An accurate automatic path-tracking via vehicle steering angle and driving/braking wheel torque is thus ensured. It combines the control of the lateral and longitudinal motions in order to track straight or curved trajectories and to perform a combined lane-keeping and steering control during critical driving situations such as obstacle avoidance, stop-and-go control, lane-change maneuvers or any other maneuvers. Promising results have been obtained with noisy experimental data, which were acquired by a laboratory vehicle with high dynamic loads and high lateral accelerations.     

Nonlinear feedback control and trajectory tracking of vehicle

This paper mainly studies nonlinear feedback control applied to the nonlinear vehicle dynamics with varying velocity. The main objective of this study is the stabilisation of longitudinal, lateral and yaw angular vehicle velocities. To this end, a nonlinear vehicle model is developed which takes both the lateral and longitudinal vehicle dynamics into account. Based on this model, a method to build a nonlinear state feedback control is first designed by which the complexity of system structure can be simplified. The obtained system is then synthesised by the combined Lyapunov–LaSalle method. The simulation results show that the proposed control can improve stability and comfort of vehicle driving. Moreover, this paper presents a lemma which ensures the trajectory tracking and path-following problem for vehicle. It can also be exploited simultaneously to solve both the tracking and path-following control problems of the vehicle ride and driving stability. We also show how the results of the lemma can be applied to solve the path-following problem, in which the vehicle converges and follows a designed path. The effectiveness of the proposed lemma for trajectory tracking is clearly demonstrated by simulation results.     

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Trajectory optimization and state selection for urban automated driving

The automated driving is an emerging technology in which a car performs recognition, decision making, and control. The decision-making system consists of route planning and trajectory planning. The route planning optimizes the shortest path to the destination like an automotive navigation system. According to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for vehicle maneuver to drive the given path. This study is focused on the trajectory planning for vehicle maneuver in urban traffic scenes. This paper proposes a trajectory generation method that extends the existing method to generate more natural behavior with small acceleration and deceleration. This paper introduces an intermediate behavior to gradually switch from the velocity keeping to the distance keeping. The proposed method can generate smooth trajectory with small acceleration/deceleration. Numerical experiments show that the vehicle generates smooth behaviors according to surrounding vehicles.     

Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles (CAVs) serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety, and reducing fuel consumption and vehicle emissions. A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads. This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service (DoS) attacks that disrupt vehicle-to-vehicle communications. First, a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties, including diverse vehicle masses and engine inertial delays, unknown and nonlinear resistance forces, and a dynamic platoon leader. Then, a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability, attack resilience, platoon safety and scalability. Furthermore, a numerically efficient offline design algorithm for determining the desired platoon control law is developed, under which the platoon resilience against DoS attacks can be maximized but the anticipated stability, safety and scalability requirements remain preserved. Finally, extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.      

智能交通系统车道保持纵横向耦合控制

考虑车辆纵横向运动之间的相互影响,采用位置预瞄和固定车辆间距跟随策略,对基于一列车队的自动化公路系统车道保持纵横向耦合控制进行了研究.利用车载前后双位置传感器检测车辆位置偏差,基于车辆纵横向动力学耦合模型,推导了基于预瞄的车道保持控制系统数学模型;采用非奇异的终端滑模控制技术,设计了车道保持纵横向耦合控制规律.通过构造李雅普诺夫函数,结合相平面方法,分析了控制系统的有限时间收敛性.采用6车辆编队,通过计算机仿真,对文中设计的控制规律进行了验证.仿真结果显示,车队中每个被控车辆在纵向上跟随期望状态的同时能够实现对期望车道轨迹的理想跟踪,跟踪误差精度不超过0.05 m.     

基于反步法的高超音速飞机纵向逆飞行控制

针对高超音速飞机纵向运动的数学模型具有严重非线性、不稳定、多变量耦合以及不确定的气动参数等特点,采用非线性动态逆控制与反步法相结合的方法为其设计飞行控制系统．该系统以非线性动态逆控制作为控制内环,通过将非线性的多输入多输出系统进行精确线性化,解除了多变量之间的强耦合关系;并以反步法作为控制外环．保证系统的全局稳定以及抑制不确定参数的扰动．仿真研究表明．所提出的控制方法可以确保高超音速飞机的纵向稳定性．改善其飞行品质．     

Lateral acceleration potential field function control for rollover safety of multi-wheel military vehicle with in-wheel-motors

This article proposes an automatic longitudinal deceleration based method for multi-wheel vehicle rollover safety in autonomous mode. The information of lateral acceleration and vehicle roll angle is used to generate the longitudinal acceleration at which the vehicle will remain stable to rollover. The lateral and roll dynamics are coupled with longitudinal dynamics using a potential field function for lateral acceleration. This virtual potential field is developed on g-g diagram which represents vehicle portrait of lateral and longitudinal acceleration on abscissa and ordinate respectively. The motion of vehicle is represented by a point moving on this phase portrait of g-g diagram. TruckSim model of multi-wheel military vehicle with in-wheel motors is used with this algorithm which shows that the vehicle is less susceptible to rollover. The safe longitudinal acceleration is achieved by torque control of in-wheel motors fitted in each wheel. Using this method, the vehicle followed the desired trajectory as higher speeds which are safe. This is particularly useful for vehicle autonomous driving with rollover stability.

     

A vehicle cloud control system considering communication quantization and stochastic delay

A novel kind of networked feedback controller is designed for the vehicle cloud control system in the presence of both sensor-controller/controller-actuator delay and communication quantization. Firstly, the vehicle cloud control system with delayed and quantized communication is modeled using the lateral/longitudinal vehicle dynamic and Markovian jump linear system (MJLS) theory. Then, some efficient stabilization conditions in matrix inequalities forms are derived for the considered cloud-controlled vehicles. Furthermore, a cone complementary linearization method is utilized to solve the nonlinear matrix inequalities involved in the stabilization conditions. Simulation tests on connected vehicle lateral and longitudinal control are conducted to verify the effectiveness of the analytical results. Compared with the commonly used constant-parameter controllers, the proposed method is practical to design vehicle cloud controllers under stochastic delay and communication quantization scenarios, with known delay distribution. Lastly, the stability of the vehicle cloud control system is guaranteed under the infection of unreliable communication factors with the proposed control method.     

Keeping in the lane! Investigating drivers’ performance handling silent vs. alerted lateral control failures in monotonous partially automated driving

This study investigated how drivers can manage the take-over when silent or alerted failure of automated lateral control occurs after monotonous hands-off driving with partial automation. Twenty-two drivers with varying levels of prior ADAS experience participated in the driving simulator experiment. The failures were injected into the driving scenario on curved road segments, accompanied by either a visual-auditory alert or no change in HMI. Results indicated that drivers could rarely maintain lane-keeping when automated steering was disabled silently, but most drivers safely managed the alerted failure situation within the ego-lane. The silent failure yielded significantly longer take-over time and generally worse lateral control quality. In contrast, poor longitudinal control performance was observed in alerted conditions due to more brake usage. An expert-based controllability assessment method was introduced to this study. The silent lateral failure situation during monotonous hands-off driving was rated as uncontrollable, while the alerted situation was basically controllable. Participants showed their preferences for the TORs, and the importance of conveying TOR reasons was also demonstrated.Relevance to industryThe results and implications of this study provided insights into the design and development of automated driving systems to prevent critical consequences. The comprehensive method of controllability assessment can benefit the automated driving system evaluation.     

Feed-forward multilayer neural network model for vehicle lateral guidance control

An advanced vehicle lateral guidance control technology is necessary in order to develop intelligent transportation and manufacturing systems with flexibility and immediate adaptability. PID control, optimal control, and fuzzy control have often been used for designing a vehicle lateral guidance controller; in addition automatic guidance methods by spline curve and inverse dynamics are also used in mobile robots (e.g. differential drive), but they are not sufficient to develop a highly intelligent vehicle lateral guidance controller which can adapt to varying environments, because they lack some behavior like learning ability and adaptability. In this paper, the possibility to apply neural networks for developing a vehicle lateral guidance controller is exposed. A new neuron activation function suitable for vehicle lateral guidance control is suggested, a feed-forward multilayer neural network (FMNN) with the suggested neuron activation function is proposed and a vehicle lateral guidance controller (VLGC) is developed by use of the FMNN. The VLGC can be applied to automobiles of different parameters and roads of various widths. It can be also applied to mobile robots. Its input variables are proposed to be defined as kind of relative quantities by using the road width, automotive parameter, automotive position, and orientation on the corner course as 90°. Its output variable is the automotive steering angle. Its teaching data are collected by automobile driving simulation, and its connection weights and threshold values are tuned through the error back-propagation algorithm. The training process and the result of neural network by different learning rate coefficients and momentum parameters are compared. Four VLGCs are generated through training by using different learning rate coefficients, momentum parameters, and repeat training times. Automated guided automobile simulations and mobile robot experiments for each VLGC are carried out. Good training result as well as automated guided simulation and experimental results are obtained.     

Nonlinear model-based track guidance of user-defined points at the vehicle front

Preventive pedestrian protection systems are validated by means of fully automated driving tests reproducing safety-critical traffic situations on a proving ground. In order to assess these preventive safety systems, a precise and reproducible collision of a pedestrian dummy with a specific point at the vehicle front, e.g., the left corner of the vehicle, must be ensured. Hence, a track guidance of this specific point is required. Beyond the state of the art a new nonlinear model describing the lateral deviation of any point at the vehicle front to a predefined path is proposed in this paper. Based on this model the method of input–output linearization is used to design a flexible lateral guidance system for an easy application in different vehicles. Furthermore, the closed-loop stability is proven and experimental results are presented.     

Four wheel steering control by fuzzy approach

This study introduces a fuzzy four-wheel steering control design method for automotive vehicles. After the analysis of some stability aspects of the vehicle lateral motion, including front steering angle variations, the representation of vehicle nonlinear model by Takagi-Sugeno (T-S) fuzzy model is presented. Next, based on the fuzzy model, a fuzzy controller is developed to improve the stability of the vehicle. Sufficient conditions for stability and stabilization of the T-S fuzzy model using fuzzy feedback controllers is given. To demonstrate the effectiveness of the proposed fuzzy controller, simulation results are given showing the performance improvements of the vehicle in terms of the stability and the maneuverability in critical situations.     

轮式机器人横向纵向统一遗传模糊神经网络控制研究

在对轮式地面机器人进行纵、横向统一遗传模糊神经网络控制中，提出了一种多目标遗传优化的策略，用分别编码、先分再合的分步优化遗传算法寻找模糊神经网络的参数。并采用先对车体动力学模型进行仿真控制，再用仿真得来的数据控制实际车辆，进行车体在线遗传寻优的方法，这种方法能最直接地控制车体运动。经过实验研究，控制效果较好，有一定意义。     

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.     

双侧电驱动履带车辆运动解耦与变结构控制

针对双侧电驱动履带车辆运动控制强非线性、强耦合和不确定性的特点,提出一种解耦的控制结构,并设计各子系统控制器.首先,将运动控制系统分解为速度、横摆角速度两个独立子系统,克服传统差速控制存在的强耦合.其次,采用积分滑模控制方法,引入非线性积分滑模面,设计了能有效克服路面不确定扰动、消除积分饱和的速度控制器,实现车速的无超调、无静差的跟踪;考虑驱动电机饱和约束,结合模糊自适应与滑模控制算法,设计了能够适应转向阻力非线性变化的横摆角速度控制器,提高车辆转向运动控制的抗扰能力、降低控制量抖振.仿真结果表明,控制策略实现多种工况下车辆快速、准确的直线、转向运动控制.     

基于变结构理论的高超音速飞机纵向逆飞行控制

针对高超音速飞机模型的高度非线性、强耦合、参数不确定等特点，提出了基于变结构理论的动态逆控制方法．该方法将逆控制的非线性解耦能力与变结构理论的强鲁棒性能有机结合，确保了高超音速飞机飞行的纵向稳定性，改善了其控制性能．仿真研究表明该控制方法对于高超音速飞机是可行的．