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。     

Lateral dynamics emulation via a four-wheel steering vehicle

In this paper, we examine the lateral dynamics emulation capabilities of an automotive vehicle equipped with four-wheel steering. We first demonstrate that the lateral dynamics of a wide range of vehicles can be emulated, either with little or with no modification on the test vehicle. Then we discuss a sliding mode controller for active front and rear wheel steering, in order to track some given yaw rate and side-slip angle. Analytically, it is shown that the proposed controller is robust to plant parameter variations by±10%, and is invariant to unmeasurable wind disturbance. The performance of the sliding mode controller is evaluated via computer simulations to verify its robustness to vehicle parameter variations and delay in the loop, and its insensitivity to wind disturbance. Finally, the emulation of a bus, a van, and two commercially available passenger vehicles is demonstrated in an advanced nonlinear simulator.     

基于混合仿真技术的车辆横向稳定性控制系统

介绍了利用先进计算机技术和仿真软件，进行车辆横向稳定性控制器设计以及系统混合仿真的过程。首先建立了8自由度车辆动力学系统模型，然后利用前馈补偿和模糊控制策略，设计了车辆横向稳定性控制器。最后对车辆横向稳定性控制系统进行了实时混合仿真研究。仿真结果表明所设计的车辆横向稳定性控制器控制有效，实时性好，而且在道路条件和行驶条件改变时具有较强的适应性和鲁棒性。     

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

ABSTRACT

Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions.     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.     

Development of an electric booster system using sliding mode control for improved braking performance

Brake systems of the future, including BBW (Brake-by-Wire), are in development in various forms. In one of the proposed hydraulic BBW systems, an electric booster system replaces the pneumatic brake booster with an electric motor and a rotational-to-linear motion mechanism. This system is able to provide improved braking performance by the design of controllers with precise target pressure tracking and control robustness for better system reliability. First, a sliding mode controller is designed using the Lyapunov function approach to secure the robustness of the system against both the model uncertainty and the disturbance caused by the master cylinder and mechanical components. Next, a simulation tool is constructed to validate the electric booster system with the proposed controller. Finally, the electric booster system is implemented into an actual brake ECU and installed in a vehicle for testing under various braking conditions. The experimental results demonstrate that the proposed controller produces faster pressure build-up performance than the conventional brake system, and its tracking performance is sufficient to ensure comfortable braking.     

汽车主动悬架的单神经元自适应控制

在1/4汽车动力学模型的基础上,设计了汽车主动悬架的自适应神经元控制器。以车辆的行驶平顺性为主要控制目标,车身垂直加速度、悬架动挠度、车轮动位移为具体评价参数,研究了系统在随机路面激励条件下的时域响应,计算了振动响应的均方根值,考察了在变参数条件下控制器的鲁棒性。仿真结果表明,该控制器能有效改善车辆的综合性能,尤其是平顺性和舒适性,并且具有较好的鲁棒性,对模型参数的变化有一定的适应性。     

液压机械无级变速器速比自抗扰控制研究

本文中设计了一种单行星排液压机械无级变速器,并为提高其速比跟踪控制性能,提出一种带前馈的自抗扰控制算法,以实现速比的跟踪控制.首先建立系统数学模型,通过传动系统转速关系的理论分析,得到前馈控制量,进一步采用自抗扰控制器对速比进行闭环控制.接着建立系统仿真平台和原型样车,通过仿真和试验对设计的带前馈自抗扰速比控制器进行验...     

汽车AMT自动离合器的改进模糊滑模控制

提出改进的模糊滑模控制器(MFSMC)实现离合器的自动控制。MFSMC采用饱和函数和模糊自适应系统来缓解抖振现象并提高其鲁棒性;采用局部线性化技术减少MFSMC的模糊规则数满足自动离合器的实时性要求。模拟结果显示所提出的控制器具有快速跟踪预定轨迹、对参数时变和外部扰动具有鲁棒性等特点。     

Stability and optimised H∞ control of tripped and untripped vehicle rollover

Vehicle rollover is a serious traffic accident. In order to accurately evaluate the possibility of untripped and some special tripped vehicle rollovers, and to prevent vehicle rollover under unpredictable variations of parameters and harsh driving conditions, a new rollover index and an anti-roll control strategy are proposed in this paper. Taking deflections of steering and suspension induced by the roll at the axles into consideration, a six degrees of freedom dynamic model is established, including lateral, yaw, roll, and vertical motions of sprung and unsprung masses. From the vehicle dynamics theory, a new rollover index is developed to predict vehicle rollover risk under both untripped and special tripped situations. This new rollover index is validated by Carsim simulations. In addition, an H-infinity controller with electro hydraulic brake system is optimised by genetic algorithm to improve the anti-rollover performance of the vehicle. The stability and robustness of the active rollover prevention control system are analysed by some numerical simulations. The results show that the control system can improve the critical speed of vehicle rollover obviously, and has a good robustness for variations in the number of passengers and longitude position of the centre of gravity.     

Lateral Collision Avoidance Robust Control of Electric Vehicles Combining a Lane-Changing Model Based on Vehicle Edge Turning Trajectory and a Vehicle Semi-Uncertainty Dynamic Model

This paper presents a new control scheme for lateral collision avoidance (CA) systems to improve the safety of four-in-wheel-motor-driven electric vehicles (FIWMD-EVs). There are two major contributions in the design of lateral CA systems. The first contribution is a new lane-changing model based on vehicle edge turning trajectory (VETT) to make vehicle adapt to different driving roads and conform to drivers’ characteristic, in addition to ensure vehicle steering safety. The second contribution is vehicle semi-uncertainty dynamic model (SUDM), which is SISO model. The problem of stability performance without the information on sideslip angle is solved by the proposed SUDM. Based on the proposed VETT and SUDM, the lateral CA system can be designed with H_∞ robust controller to restrain the effect of uncertainties resulting from parameter perturbation and lateral wind disturbance. Single and mixed driving cycles simulation experiments are carried out with CarSim to demonstrate the effectiveness in control scheme, simplicity in structure for lateral CA system based on the proposed VETT and SUDM.     

Model-independent self-tuning fault-tolerant control method for 4WID EV

To solve the problem of the existing fault-tolerant control system of four-wheel independent drive (4WID) electric vehicles (EV), which relies on fault diagnosis information and has limited response to failure modes, a modelindependent self-tuning fault-tolerant control method is proposed. The method applies model-independent adaptive control theory for the self-tuning active fault-tolerant control of a vehicle system. With the nonlinear properties of the adaptive control, the complex and nonlinear issues of a vehicle system model can be solved. Besides, using the online parameter identification properties, the requirement of accurate diagnosis information is relaxed. No detailed model is required for the controller, thereby simplifying the development of the controller. The system robustness is improved by the error based method, and the error convergence and input-output bounds are proved via stability analysis. The simulation and experimental results demonstrate that the proposed fault-tolerant control method can improve the vehicle safety and enhance the longitudinal and lateral tracking ability under different failure conditions.     

FSAC赛车轨迹跟踪协同控制策略研究

针对中国大学生方程式赛车（FSAC）在比赛中横向-纵向协同控制的轨迹跟踪精度和稳定性问题,根据现代控制理论和经典控制理论提出一种以纵向速度为结合点的线性二次控制器（LQR）和比例-积分-微分算法（PID）的横纵向协同控制策略,并根据赛车相对参考轨迹的位置设计了一种协同控制器。建立二自由度车辆动力学模型,基于该模型设计了横向LQR位置跟踪控制器和纵向PID速度跟踪控制器。所设计的控制策略在CarSim和Simulink搭建的循迹工况联合仿真场景下进行仿真验证,仿真结果为纵向位置偏差小于0.07 m,横向位置偏差小于0.03 m。对控制算法进行实车验证,结果表明,该策略有效提高了赛车的轨迹跟踪精度和行驶稳定性。     

基于小波控制的电动汽车稳定性研究（双语出版）

针对前轮独立驱动电动汽车,研究一种基于小波控制器的驱动稳定性控制系统。为提高车辆对开路面的行驶稳定性,根据驱动轮等转矩分配控制策略,提出基于神经网络PID的驱动轮滑移率相近为目标控制策略。针对矢量控制中的电流控制,提出基于离散小波变换的电流控制器。通过CarSim/Simulink建立前轮独立驱动电动汽车联合仿真平台,进行不同工况整车性能仿真与分析,并基于A&D5435快速原型开发平台进行实车试验。仿真与试验结果表明：基于小波控制器的驱动控制系统不仅提高了车辆对开路面行驶的稳定性,而且具有更平滑、更快速的转矩响应;对开路面工况下,提出的控制策略左侧、右侧驱动轮速度仿真结果与试验结果最大偏差分别为3.43%和3.56%;等转矩分配控制策略下,左侧、右侧驱动轮速度仿真结果与试验结果最大偏差分别为3.86%和3.25%,表明了试验与仿真的一致性;对开路面仿真工况下,相比于驱动轮等转矩分配控制策略,基于神经网络PID的驱动轮滑移率相近为目标控制策略的车辆峰值质心侧偏角降低了79.57%,侧向跑偏距离降低了73.39%。     

汽车防抱死制动系统H_∞控制与PID控制的比较研究

控制方法是汽车防抱死制动系统的核心技术。为了提高ABS系统的鲁棒性能,在建立汽车防抱死制动系统数学模型的基础上,设计了H∞控制器,在Matlab/Simulink平台上对基于H∞控制器的ABS系统进行了动态仿真,并与基于传统PID控制器的ABS系统进行对比。通过对仿真结果进行比较发现,PID控制和H∞控制都能使ABS系统获得较好的制动性能;H∞控制响应迅速、具有优秀的稳定性和鲁棒性,总体控制效果优于PID控制。     

无人驾驶机器人车辆非线性模糊滑模车速控制

为了实现不同行驶工况下车速的精确、稳定控制,提出一种基于非线性干扰观测器的无人驾驶机器人车辆模糊滑模车速控制方法。考虑模型不确定性和外部干扰对车速控制的影响,建立车辆纵向动力学模型。通过分析无人驾驶机器人油门机械腿、制动机械腿的结构、机械腿操纵自动挡车辆踏板的运动,建立油门机械腿和制动机械腿的运动学模型。在此基础上,分别设计油门/制动切换控制器、油门模糊滑模控制器以及制动模糊滑模控制器,并进行控制系统的稳定性分析。油门/制动切换控制器以目标车速的导数为输入来进行油门与制动之间的切换控制。油门模糊滑模控制器和制动模糊滑模控制器以当前车速以及车速误差为输入,分别以油门机械腿直线电机位移和制动机械腿直线电机位移为输出来实现对油门与制动的控制。模糊滑模控制器中,为了减少控制抖振,滑模控制的反馈增益系数由模糊逻辑进行在线调节。模糊滑模控制器中的非线性干扰观测器用于估计和补偿无人驾驶机器人车辆的模型不确定性与外部干扰。仿真及试验结果对比分析表明：本文方法能够精确地估计和补偿无人驾驶机器人车辆的模型不确定性和外部干扰,避免了油门控制与制动控制之间的频繁切换,并实现了精确稳定的车速控制。     

Force control for path following of a 4WS4WD vehicle by the integration of PSO and SMC

The aim of this paper is to present a novel control method for a four-wheel steer and four-wheel drive (4WS4WD) vehicle. The novelty is in the integration of sliding mode control (SMC) and particle swarm optimization (PSO) that is proposed to solve the control problem caused by the nonlinear, highly coupled and over-actuated characteristics of the four-wheel steer and four-wheel drive (4WS4WD) vehicle. The validity of the control method is evaluated by two criterions, namely path following performance assessed by the vehicle's position errors with respect to the reference path, and motion quality reflected by the smoothness of vehicle's velocities and accelerations. In vehicle modelling, a kinematic model and a dynamic model considering all slip forces are proposed for the controller design. Simulation results are provided to demonstrate the applicability of the proposed methodology and its robustness.     

车辆防抱系统鲁棒控制的研究

本文讨论了防抱制动系统鲁棒控制器设计问题，采用鲁棒和性能加权，系统有效地抗干扰和参数变化，使控制器有效地适应不同的路况及制动工况，与ＰＩＤ算法进行了比较，模拟的结果证实了该控制器的优良品质。     

Active control strategy for the running attitude of high-speed train under strong crosswind condition

This paper focuses on the safety of high-speed trains under strong crosswind conditions. A new active control strategy is proposed based on the adaptive predictive control theory. The new control strategy aims at adjusting the attitudes of a train by controlling the new-type intelligent giant magnetostrictive actuator (GMA). It combined adaptive control with dynamic matrix control; parameters of predictive controller was real-time adjusted by online distinguishing to enhance the robustness of the control algorithm. On this basis, a correction control algorithm is also designed to regulate the parameters of predictive controller based on the step response of a controlled objective. Finally, the simulation results show that the proposed control strategy can adjust the running attitudes of high-speed trains under strong crosswind conditions; they also indicate that the new active control strategy is effective and applicable in improving the safety performance of a train based on a host–target computer technology provided by Matlab/Simulink.     

基于单神经元的汽车方向自适应PID控制

针对汽车方向动力学控制存在的非线性和参数时变不确定性问题，提出了一种新的基于单神经元的汽车方向自适应PID控制算法。该算法利用了神经网络的自学习和自适应能力，实现了方向PID控制器的参数在线自整定，从而避免了传统的自适应PID控制必须在线辨识被控系统的参考模型参数而带来的计算工作量大的问题。仿真计算和场地试验验证表明该控制算法可有效地控制汽车按照预期给定的轨迹行驶，且保证了汽车方向闭环控制系统具有较强的适应性和鲁棒性。