Feedback brake distribution control for minimum pitch

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution.     

Modelling minimum-time manoeuvering with global optimisation of local receding horizon control

In this paper, we explore the notion that a human driver uses a receding horizon model predictive control (MPC) scheme for minimum-time manoeuvering. However, MPC is an inherently sub-optimal control scheme because not all future information is incorporated into its finite preview horizon. In many practical applications, this sub-optimality is tolerated as the solution is sufficiently close to optimal. However, it is known that professional drivers have the ability to learn driving circuits and exploit its features to minimise their global manoeuvering time. In this paper, we will model their process with a cascaded optimisation structure. Therein, the inner-loop features a local MPC scheme tasked with finding the control inputs that achieve a blended objective of minimising time and maximising velocity in each preview horizon/distance. The outer loop of this cascaded structure computes the best set of weights for the two components of the local objectives in order to minimise the global manoeuvering time. The proposed cascaded optimisation and control approach is compared against a straight-forward fixed-cost time optimal MPC applied to minimum-time manoeuvering over two well-known race courses. The paper also includes an extended literature review and details of the computational formulation of the model approach.     

Model predictive driving simulator motion cueing algorithm with actuator-based constraints

The simulator motion cueing problem has been considered extensively in the literature; approaches based on linear filtering and optimal control have been presented and shown to perform reasonably well. More recently, model predictive control (MPC) has been considered as a variant of the optimal control approach; MPC is perhaps an obvious candidate for motion cueing due to its ability to deal with constraints, in this case the platform workspace boundary. This paper presents an MPC-based cueing algorithm that, unlike other algorithms, uses the actuator positions and velocities as the constraints. The result is a cueing algorithm that can make better use of the platform workspace whilst ensuring that its bounds are never exceeded. The algorithm is shown to perform well against the classical cueing algorithm and an algorithm previously proposed by the authors, both in simulation and in tests with human drivers.     

基于微分几何的四轮独立驱动车辆运动解耦控制研究

针对车辆在纵向运动和横摆运动时的强耦合关系给车辆动力学控制带来的困难，以四轮独立电驱动车辆作为研究对象，基于微分几何理论设计了车辆系统运动解耦控制方法，将非线性强耦合的四轮驱动车辆动力学系统解耦为纵向和横向两个相对独立运动控制子系统，并设计了鲁棒控制器，以提高抵抗车辆行驶时不确定外力如侧风的干扰能力。基于 Trucksim 软件建立四轮驱动车辆模型，并针对车辆解耦控制策略和抗干扰策略进行了仿真测试。结果表明，相比于无解耦控制的车辆，采用微分几何解耦控制的四轮独立驱动车辆纵向速度偏差降低了 82.1%，横摆角速度偏差降低了80.7%，且微风干扰下的抗干扰能力明显改善，车辆稳定性显著提升。为验证该运动解耦控制策略在实时系统中的控制效果，还进行了硬件在环试验，结果表明，硬件在环试验的结果与仿真结果一致。     

基于自适应模型预测的智能汽车横向轨迹跟踪控制

当路面附着情况和车辆行驶状态不断变化时,基于恒定侧偏刚度的模型预测控制(MPC)不能考虑轮胎非线性特性的影响,难以保证车辆轨迹跟踪的适应性。为此,提出一种考虑轮胎侧向力计算误差的自适应模型预测控制(AMPC),以提高智能汽车在不确定工况下的轨迹跟踪性能。分析了路面附着系数和垂向载荷对轮胎侧向力的影响,基于平方根容积卡尔曼滤波(SCKF)算法,设计了利用侧向加速度和横摆角速度作为测量变量的前后轮胎侧向力估计器。利用轮胎侧向力线性计算值与估计值的差值计算得到侧偏刚度修正因子,设计了前后轮胎侧偏刚度的自适应修正准则,进而提出了一种基于时变修正刚度的AMPC控制方法。基于CarSim与MATLAB/Simulink联合仿真和硬件在环测试平台,对AMPC控制的有效性和实时性进行了验证。研究结果表明：在不同的路面附着情况和车辆行驶状态下,AMPC控制都能够降低横向位置偏差和航向角偏差,有效提高车辆的轨迹跟踪精度,其控制效果明显优于基于恒定侧偏刚度的标准MPC控制。尤其在低附着工况下,标准MPC控制会因为线性轮胎力的计算误差过大而导致车辆在轨迹跟踪时严重失稳,而AMPC控制通过估计轮胎力修正侧偏刚度依然能够保证车辆稳定有效的跟踪参考轨迹。所提出的AMPC控制在保证控制精度的同时具有良好的实时性,对智能汽车控制系统的设计与优化具有重要参考价值。     

四轮转向汽车自适应模型跟踪控制研究

使用单点预瞄驾驶员模型，针对确定性汽车模型探讨了４ＷＳ汽车在单移线行驶过程中后轮的最优转向控制规律。通过引入状态反馈，改善了整车的转向特性，将实际汽车的前后轮胎侧刚度及外界干扰视为有界的不确定性参数，采用自适应模型跟踪变结构控制方法，使得不确定的实际汽车模型能够很好地跟踪确定的最优理论模型，仿真结果表明该方法的可行性，控制系统能够有效地克服参数摄动及外界干扰对系统稳定性的影响。     

商用车电液复合转向系统的车道保持策略

为了提高商用车的行驶安全性,避免因驾驶人的分心驾驶出现车辆偏离车道的问题,提出一种基于电液复合转向系统的商用车车道保持策略;在建立电液复合转向系统模型、二自由度车辆模型、预瞄驾驶人模型的基础上,设计基于驾驶人在环的MPC和ADRC串级的车道保持控制策略。首先,采用MPC算法将车辆横向位置控制的最优问题转化为二次规划求得目标前轮转角;然后,考虑电液复合转向系统的不确定和干扰问题,利用ADRC算法对目标转向盘转角和实际驾驶人的转向盘转角差值以转矩信号的形式进行补偿。同时研究车道保持系统对驾驶人的干预问题,引入干预系数的概念,采用模糊控制的方法,将驾驶人手力和车辆的运动状态作为输入变量,干预系数作为输出变量,保证整车行驶安全性的前提下减小车道保持辅助系统对驾驶人的干预。最后,通过MATLAB/Simulink仿真和硬件在环试验对所设计的控制策略进行验证。研究结果表明：所设计的基于商用车电液复合转向系统的车道保持策略能够及时地纠正因驾驶人的分心驾驶而导致车辆偏离所在行驶车道的行为,特别是在弯道处出现驾驶人转向不足或过度转向的情况时,能够将车辆维持在车道线之内,保证车辆的行驶安全性,同时由于干预系数的设计,使得驾驶人也有良好的人机交互体验感。     

汽车非线性半主动悬架的模糊神经网络控制

考虑磁流变减振器阻尼力和悬架弹性元件非线性特性，建立车辆6自由度的半主动悬架非线性动力学模型。提出了一种基于模糊神经网络系统结构的模型参考自适应控制方法来研究汽车半主动悬架的非线性控制问题，并考虑半车模型前后悬架的输入时滞，对其进行了仿真研究。研究结果表明：运用模糊神经网络非线性控制方法能够使人体和车身垂直加速度、俯仰角加速度都得到很大的衰减，证实这种模糊神经网络控制方法可大大减少路面对车身的振动冲击，提高汽车行驶平顺性。     

Comparing rear wheel steering and rear active differential approaches to vehicle yaw control

A comparison between two different approaches to vehicle stability control is carried out, employing a robust non-parametric technique in the controller design. In particular, an enhanced internal model control strategy, together with a feedforward action and a suitably generated reference map, is employed for the control of a vehicle equipped either with a rear wheel steering (RWS) system or with a rear active differential (RAD) device. The uncertainty arising from the wide range of operating conditions is described by an additive model set employed in the controller design. Extensive steady state and transient tests simulated with an accurate 14 degrees of freedom nonlinear model of the considered vehicle show that both systems are able to improve handling and safety in normal driving conditions. RAD devices can make the vehicle reach higher lateral acceleration values but they achieve only slight stability improvements against oversteer. On the other hand, 4WS systems can greatly improve both vehicle safety and manoeuvrability in all driving situations, making this device an interesting and powerful stability system.     

A theoretical justification of the sine with dwell manoeuvre

The sine with dwell (SWD) manoeuvre has received much attention within the context of vehicle stability testing. This manoeuvre is used in a test developed by the NHTSA, designed to certificate electronic stability control systems in light vehicles. The test is used in legislations as well as in consumer tests all over the globe. The SWD manoeuvre was designed using test vehicles on a test track and experimentally validated. The paper at hand uses optimal control theory to theoretically justify the use of the SWD manoeuvre to produce a severe lateral motion and over-steering based on steering input. It is shown that a manoeuvre similar to the SWD manoeuvre can be obtained from an optimal control problem using simple vehicle dynamics models. The optimal control method is further used to analyse the manoeuvre's robustness with respect to vehicle dimensions and tyre properties. It is shown that the manoeuvre is robust in dimensions, which theoretically motivates its application for various sizes of vehicles.     

基于动态门限值的分布式驱动电动汽车驱动力矩控制研究

为改善分布式驱动电动汽车高速行驶稳定性，避免频繁驱动控制操作对汽车行驶安全性的影响，提出了一种适应不同驾驶工况的参数动态门限值算法，设计了汽车附加横摆力矩滑模控制策略和驱动力矩二次规划优化分配控制策略，并进行了角阶跃输入工况和双正弦输入工况的仿真分析。结果表明，所设计的控制策略能有效控制汽车的质心侧偏角与横摆角速度，在保证汽车行驶稳定性的前提下，使质心侧偏角与理想值偏差减小了3.6%以上，轮胎附着利用率减少19.5%以上，有效地降低了轮胎附着利用率，提高了汽车的行驶安全性。     

复合功率分流系统发动机起动H∞鲁棒优化控制

为改善复合功率分流混合动力系统纯电动至混合动力模式切换过程的车辆驾驶平顺性,同时确保在模型不确定和外部干扰条件下切换控制的鲁棒性,本文中提出了一种发动机起动H∞鲁棒控制策略。首先,建立复合功率分流混合动力系统动力学模型,并对纯电动至混合动力模式切换过程进行分析。其次,以车辆驾驶平顺性和发动机起动时间为优化指标,通过动态规划求解发动机最优拖转转速曲线。然后,考虑到输入轴阻尼参数摄动、驾驶员输入、道路负载、输出端转速的不确定性变化和发动机转速量测噪声的干扰,设计了发动机起动H∞鲁棒控制器。最后,通过离线仿真和台架试验对所提出的控制策略进行验证。结果表明,该策略能有效将冲击度降低至11.52 m/s^3以内,同时对模型不确定性和外部干扰有较强的抑制能力。     

Lane-keeping benefits of practical rear axle steer

The classic two-degree-of-freedom yaw-plane or ‘bicycle’ vehicle model is augmented with two additional states to describe lane-keeping behaviour and further augmented with an additional control input to steer the rear axle. A simple driver model is hypothesised where the driver closes a loop on a projected lateral lane position. The driver can select the preview distance to compensate driver/vehicle dynamics, consistent with the ‘cross-over’ model found in the literature. A rear axle steer control law is found to be a function of the front axle steering input and vehicle speed that exhibits stability similar to a positive-real system, while at the same time improving the ability of the driver/vehicle system to track a complex curved lane and improving steady-state manoeuvrability. The theoretically derived control law bears similarity to practical embodiments allowing a deeper understanding of the functional value of steering a rear axle.     

Lateral handling improvement with dynamic curvature control for an independent rear wheel drive EV

The integrated longitudinal and lateral dynamic motion control is important for four wheel independent drive (4WID) electric vehicles. Under critical driving conditions, direct yaw moment control (DYC) has been proved as effective for vehicle handling stability and maneuverability by implementing optimized torque distribution of each wheel, especially with independent wheel drive electric vehicles. The intended vehicle path upon driver steering input is heavily depending on the instantaneous vehicle speed, body side slip and yaw rate of a vehicle, which can directly affect the steering effort of driver. In this paper, we propose a dynamic curvature controller (DCC) by applying a the dynamic curvature of the path, derived from vehicle dynamic state variables; yaw rate, side slip angle, and speed of a vehicle. The proposed controller, combined with DYC and wheel longitudinal slip control, is to utilize the dynamic curvature as a target control parameter for a feedback, avoiding estimating the vehicle side-slip angle. The effectiveness of the proposed controller, in view of stability and improved handling, has been validated with numerical simulations and a series of experiments during cornering engaging a disturbance torque driven by two rear independent in-wheel motors of a 4WD micro electric vehicle.     

基于人机共驾的车道保持辅助控制系统研究（双语出版）

车道保持控制系统是汽车安全辅助驾驶的重要组成部分，可有效提高汽车主动安全性、避免车辆无意识地偏离本车道。目前，大部分车道保持控制系统在工作时将驾驶人的操作视为外界干扰，没有考虑人机共驾阶段下驾驶人与控制系统的控制权分配问题，易造成人机冲突、影响驾驶人的驾驶感受。论文兼顾驾驶人与辅助控制系统各自优势，基于人机共驾技术对车道保持控制系统进行研究。构建基于安全行驶区域与最晚预警边界相结合的车道偏离决策模型，在保证其预警精度的同时降低计算复杂性，根据车辆行驶状态和路面附着系数动态调整预警阈值；研究串级MPC-PID控制策略实现对车辆横向位置的控制，将最优问题转化为二次规划求得目标前轮转角，利用PID算法完成对目标前轮转角的跟踪；引入共驾系数对车辆的控制权进行分配，研究共驾系数分配模型，以车辆状态误差和驾驶人转向力矩作为模糊控制的输入变量、共驾系数作为输出变量，降低辅助控制系统与驾驶人之间的冲突；最后，利用CarSim与Simulink联合仿真对所研究的控制策略进行仿真验证，结果表明共驾系数能够根据驾驶人的操作和车辆运行状态的变化实现动态调整，辅助控制力矩与驾驶人输入力矩变化趋势相同，在保留驾驶人一定操作的基础下可避免车辆偏离车道、降低人机冲突。     

Nonlinear optimal integrated vehicle control using individual braking torque and steering angle with on-line control allocation by using state-dependent Riccati equation technique

This paper proposes the solution of state-dependent Riccati equation as a nonlinear optimal regulator to stabilise the motion dynamics of the vehicle model subjected to sudden disturbance inputs in the lateral direction. The proposed nonlinear regulator coordinates individually actuated wheel braking torque and steering wheel angle simultaneously in an optimal manner. Performance criteria are satisfied by solving the Riccati equation based on the given cost function subjected to the nonlinear vehicle dynamics. On-line control allocation in terms of optimal brake torque distribution enhanced by optimal wheel steering angle input is achieved. Furthermore, the proposed optimal nonlinear regulator is an active fault-tolerant control system against partial by-wire actuator failures while guaranteeing stability with good performance due to its capability to allocate the individual control inputs in an optimal way. The main aim is to stabilise the motion dynamics of the vehicle model during short-term emergency situations along the desired straight trajectory manageable by average drivers and to provide vehicle stability and handling predictability through the interaction of individual wheel braking and steering actuators. Simulation results are used to illustrate the effectiveness of the proposed methodology.     

Wheel slip control with torque blending using linear and nonlinear model predictive control

Modern hybrid electric vehicles employ electric braking to recuperate energy during deceleration. However, currently anti-lock braking system (ABS) functionality is delivered solely by friction brakes. Hence regenerative braking is typically deactivated at a low deceleration threshold in case high slip develops at the wheels and ABS activation is required. If blending of friction and electric braking can be achieved during ABS events, there would be no need to impose conservative thresholds for deactivation of regenerative braking and the recuperation capacity of the vehicle would increase significantly. In addition, electric actuators are typically significantly faster responding and would deliver better control of wheel slip than friction brakes. In this work we present a control strategy for ABS on a fully electric vehicle with each wheel independently driven by an electric machine and friction brake independently applied at each wheel. In particular we develop linear and nonlinear model predictive control strategies for optimal performance and enforcement of critical control and state constraints. The capability for real-time implementation of these controllers is assessed and their performance is validated in high fidelity simulation.     

基于模型预测控制的半挂汽车主动防侧倾控制方法

提出了一种半挂汽车主动防侧倾控制方法。搭建了一个七自由度动力学模型和一个三自由度参考模型;用无迹Kalman滤波的方法,来估计车辆的横向载荷转移率;确定优化目标,运用模型预测控制(MPC)理论进行最优化求解,得到各车轴的主动防侧倾力矩;在Simulink/Trucksim联合仿真环境中,进行仿真对比与分析。结果表明:在本文的MPC控制器和PID控制器作用下,半挂汽车各状态量皆收敛,且横向载荷转移率保持在0.7以内;相比于PID控制,MPC控制所需的防侧倾力矩更小更均衡,各状态量变化也更加平稳。因此,本MPC控制器在提升半挂汽车侧倾稳定性的同时具有较好的鲁棒性。     

A yaw-moment control method based on a vehicle's lateral jerk information

Previously, a new control concept called ‘G-vectoring control (GVC)’ to improve vehicle agility and stability was developed. GVC is an automatic longitudinal acceleration control method that responds to vehicle lateral jerk caused by the driver's steering manoeuvres. In this paper, a new yaw-moment control method, which generates a stabilising moment during the GVC command and has positive acceleration value and the driver's accelerator pedal input is zero, was proposed. A new hybrid control, which comprises GVC, electric stability control and this new control, was constructed, and it was installed in a test vehicle and tested on a snowy surface. The very high potential for improvement in both agility and stability was confirmed.     

面向全矢量线控车辆极限运动的分层式协同控制

装配四轮分布式驱动-转向（4WID-4WIS）底盘的全矢量线控车辆具备多可控自由度、高速稳定性强的特点,是极限工况稳定裕度和安全性较高的理想车型。为了解决全矢量线控车辆在极限工况下纵横向控制冲突危害行车安全的问题,提出一种基于模型预测控制 （MPC） 的分层式车辆纵向和横向运动协同控制方法。建立基于单轨模型的期望运动状态识别方法,设计模型预测控制器转换动力学目标,采用泰勒展开和前向欧拉方法对预测模型进行线性离散化处理;设计基于负荷率的轮胎力优化分配方法,利用反正切轮胎逆模型求解控制执行量。仿真结果表明,协同控制方法能显著提高车辆在不同路面下的极限运动稳定性,更精准地跟踪期望运动状态,扩大稳定裕度,保障行车安全。