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。     

Cooperative control of the motor and the electric booster brake to improve the stability of an in-wheel electric vehicle

A cooperative control algorithm for an in-wheel motor and an electric booster brake is proposed to improve the stability of an in-wheel electric vehicle. The in-wheel system was modeled by dividing it into motor and mechanical parts, and the electric booster brake was modeled through tests. In addition, the response characteristics of the in-wheel system and the electric booster brake were compared through a frequency response analysis. In the cooperative control, the road friction coefficient was estimated using the wheel speed, motor torque, and braking torque of each wheel, and the torque limit of the wheel to the road was determined using the estimated road friction coefficient. Based on the estimated road friction coefficient and torque limit, a cooperative algorithm to control the motor and the electric booster brake was proposed to improve the stability of the in-wheel electric vehicle. The performance of the proposed cooperative control algorithm was evaluated through a hardware-in-the-loop simulation (HILS). Furthermore, to verify the performance of the proposed cooperative control algorithm, a test environment was constructed for the anti-lock braking system (ABS) hydraulic module hardware, and the performance of the cooperative control algorithm was compared with that of the ABS by means of a HILS test.     

Heavy-duty vehicle modelling and longitudinal control

This paper addresses modelling, longitudinal control design and implementation for heavy-duty vehicles (HDVs). The challenging problems here are: (a) an HDV is mass dominant with low power to mass ratio; (b) They possess large actuator delay and actuator saturation. To reduce model mismatch, it is necessary to obtain a nonlinear model which is as simple as the control design method can handle and as complicated as necessary to capture the intrinsic vehicle dynamics. A second order nonlinear vehicle body dynamical model is adopted, which is feedback linearizable. Beside the vehicle dynamics, other main dynamical components along the power-train and drive-train are also modelled, which include turbocharged diesel engine, torque converter, transmission, transmission retarder, pneumatic brake and tyre. The braking system is the most challenging part for control design, which contains three parts: Jake (engine compression) brake, air brake and transmission retarder. The modelling for each is provided. The use of engine braking effect is new complementary to Jake (compression) brake for longitudinal control, which is united with Jake brake in modelling. The control structure can be divided into upper level and lower level. Upper level control uses sliding mode control to generate the desired torque from the desired vehicle acceleration. Lower level control is divided into two branches: (a) engine control: from positive desired torque to desired fuel rate (engine control) using a static engine mapping which basically captures the intrinsic dynamic performance of the turbo-charged diesel engine; (b) brake control: from desired negative torque to generate Jake brake cylinder number to be activated and ON/OFF time periods, applied pneumatic brake pressure and applied voltage of transmission retarder. Test results are also reported.     

Heavy-duty vehicle modelling and longitudinal control

This paper addresses modelling, longitudinal control design and implementation for heavy-duty vehicles (HDVs). The challenging problems here are: (a) an HDV is mass dominant with low power to mass ratio; (b) They possess large actuator delay and actuator saturation. To reduce model mismatch, it is necessary to obtain a nonlinear model which is as simple as the control design method can handle and as complicated as necessary to capture the intrinsic vehicle dynamics. A second order nonlinear vehicle body dynamical model is adopted, which is feedback linearizable. Beside the vehicle dynamics, other main dynamical components along the power-train and drive-train are also modelled, which include turbocharged diesel engine, torque converter, transmission, transmission retarder, pneumatic brake and tyre. The braking system is the most challenging part for control design, which contains three parts: Jake (engine compression) brake, air brake and transmission retarder. The modelling for each is provided. The use of engine braking effect is new complementary to Jake (compression) brake for longitudinal control, which is united with Jake brake in modelling. The control structure can be divided into upper level and lower level. Upper level control uses sliding mode control to generate the desired torque from the desired vehicle acceleration. Lower level control is divided into two branches: (a) engine control: from positive desired torque to desired fuel rate (engine control) using a static engine mapping which basically captures the intrinsic dynamic performance of the turbo-charged diesel engine; (b) brake control: from desired negative torque to generate Jake brake cylinder number to be activated and ON/OFF time periods, applied pneumatic brake pressure and applied voltage of transmission retarder. Test results are also reported.     

A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations

A sliding-mode observer is designed to estimate the vehicle velocity with the measured vehicle acceleration, the wheel speeds and the braking torques. Based on the Burckhardt tyre model, the extended Kalman filter is designed to estimate the parameters of the Burckhardt model with the estimated vehicle velocity, the measured wheel speeds and the vehicle acceleration. According to the estimated parameters of the Burckhardt tyre model, the tyre/road friction coefficients and the optimal slip ratios are calculated. A vehicle adaptive sliding-mode control (SMC) algorithm is presented with the estimated vehicle velocity, the tyre/road friction coefficients and the optimal slip ratios. And the adjustment method of the sliding-mode gain factors is discussed. Based on the adaptive SMC algorithm, a vehicle's antilock braking system (ABS) control system model is built with the Simulink Toolbox. Under the single-road condition as well as the different road conditions, the performance of the vehicle ABS system is simulated with the vehicle velocity observer, the tyre/road friction coefficient estimator and the adaptive SMC algorithm. The results indicate that the estimated errors of the vehicle velocity and the tyre/road friction coefficients are acceptable and the vehicle ABS adaptive SMC algorithm is effective. So the proposed adaptive SMC algorithm can be used to control the vehicle ABS without the information of the vehicle velocity and the road conditions.     

Robust control of anti-lock brake system

A robust control algorithm for an anti-lock brake system is proposed. The method used is based on static-state feedback of longitudinal slip and does not involve controller scheduling with changing vehicle speed or road adhesion coefficient estimation. An improvement involving scheduling of longitudinal slip reference with longitudinal acceleration measurement is included. Electromechanical braking actuators are used in simulations, and the algorithm used in this study is shown to have high performance on roads with constant and varying adhesion coefficients, displaying nice robustness properties against large vehicle speed and road adhesion coefficient variations. Guidelines are provided for tuning controller gains to cope with unknown actuator delay and measurement noise.     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

牵引力控制系统模糊PI控制方法研究

提出牵引力控制系统的油门模糊增量PI控制算法和驱动轮制动模糊PI控制算法。在建立汽车加速过程的发动机模型、制动器模型、轮胎模型、整车模型和路面自动识别系统的基础上，采用所提出的控制算法对典型附着路面上的牵引力控制过程进行了仿真模拟。结果表明路面自动识别系统能识别路面附着变化，有效抑制各种工况的驱动轮过度滑转，有较强的鲁棒性。     

发动机制动、排气制动与缓速器联合作用时的非连续线性控制系统的研究

针对客车发动机制动、排气制动的制动扭矩比较小的问题,提出采用发动机制动、排气制动与缓速器联合作用的持续制动方式,并且针对汽车在山区道路下坡行驶过程中对稳定车速的要求,进行了相应的控制系统设计。模拟分析结果表明:该控制系统可以保证汽车在不采用行车制动器的条件下,利用发动机制动、排气制动与缓速器联合作用的持续制动方式,在各种坡度的坡道上以希望的车速稳定下坡行驶,为汽车在山区道路连续下坡行驶的制动安全性提供了一个合理的解决方案。     

eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control

In this paper, evolving Takagi-Sugeno (eTS) fuzzy driver model is proposed for simultaneous lateral and longitudinal control of a vehicle in a test track closed to traffic. The developed eTS fuzzy driver model can capture human operator’s driving expertise for generating desired steering angle, throttle angle and brake pedal command values by processing only information which can be supplied by the vehicle’s on-board control systems in real time. Apart from other fuzzy rule based (FRB) models requiring human expert knowledge or off-line clustering, the developed eTS driver model can adapt itself automatically, even ‘from scratch’, by an on-line learning process using eTS algorithm while human driver is supervising the vehicle. Proposed eTS fuzzy driver model’s on-line human driver identification capability and autonomous vehicle driving performance were evaluated on real road profiles created by digitizing two different intercity express ways of Turkey in IPG© CarMaker® software. The training and validation simulation results demonstrated that eTS fuzzy driver model can be used in product development phase to speed up different tests via realistic simulations. Furthermore eTS fuzzy driver model has an application potential in the field of autonomous driving.     

基于双模式执行器的商用车自适应巡航控制系统

为实现商用车自适应巡航控制(ACC)系统的功能,开发了双模式制动执行装置和电子油门控制装置,即基于高速开关阀的商用车气压电控辅助制动系统和双模式油门控制系统,可以实现驾驶员和ACC系统的协同切换控制。在此基础上,以某商用车为对象,设计了ACC系统,结合比例-积分控制器和Smith预估补偿器设计了ACC的下位控制算法。结果表明:该ACC系统速度稳态跟踪误差小于1 m.s-1,距离稳态跟踪误差小于1.5 m;同时油门执行器和制动执行器具有安装方便、与原车电子油门及气压制动系统兼容性好的优点。     

Model predictive control with constraints for a nonlinear adaptive cruise control vehicle model in transition manoeuvres

A vehicle following control law, based on the model predictive control method, to perform transition manoeuvres (TMs) for a nonlinear adaptive cruise control (ACC) vehicle is presented in this paper. The TM controller ultimately establishes a steady-state following distance behind a preceding vehicle to avoid collision, keeping account of acceleration limits, safe distance, and state constraints. The vehicle dynamics model is for continuous-time domain and captures the real dynamics of the sub-vehicle models for steady-state and transient operations. The ACC vehicle can execute the TM successfully and achieves a steady-state in the presence of complex dynamics within the constraint boundaries.     

Extended-Kalman-filter-based regenerative and friction blended braking control for electric vehicle equipped with axle motor considering damping and elastic properties of electric powertrain

Because of the damping and elastic properties of an electrified powertrain, the regenerative brake of an electric vehicle (EV) is very different from a conventional friction brake with respect to the system dynamics. The flexibility of an electric drivetrain would have a negative effect on the blended brake control performance. In this study, models of the powertrain system of an electric car equipped with an axle motor are developed. Based on these models, the transfer characteristics of the motor torque in the driveline and its effect on blended braking control performance are analysed. To further enhance a vehicle's brake performance and energy efficiency, blended braking control algorithms with compensation for the powertrain flexibility are proposed using an extended Kalman filter. These algorithms are simulated under normal deceleration braking. The results show that the brake performance and blended braking control accuracy of the vehicle are significantly enhanced by the newly proposed algorithms.     

基于预测恒定车头时距策略的自适应巡航控制研究

为了进一步提高车辆跟车过程中的跟踪性、安全性、舒适性和燃油经济性，针对已有间距策略表现过于保守或反应过于激烈等不足之处，提出了一种预测恒定车头时距策略。该策略考虑了相对加速度，建立了一种预测型期望车间距模型，进而应用于模型预测控制的多目标自适应巡航控制系统中，能进一步提高模型预测控制对多个控制目标的综合协调能力。搭建上层控制器、下层PID控制器、油门制动切换、逆纵向动力学模型。在多工况下仿真，通过建立性能评判指标对多目标进行量化分析。结果表明，所提出的间距策略在保证安全性的前提下，提升了自适应巡航控制系统的综合性能。在不同驾驶风格的车头时距下，跟踪性、舒适性和燃油经济性均有良好表现。     

Fuzzy-logic applied to yaw moment control for vehicle stability

In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).     

Fuzzy-logic applied to yaw moment control for vehicle stability

In this paper, we propose a new yaw moment control based on fuzzy logic to improve vehicle handling and stability. The advantages of fuzzy methods are their simplicity and their good performance in controlling non-linear systems. The developed controller generates the suitable yaw moment which is obtained from the difference of the brake forces between the front wheels so that the vehicle follows the target values of the yaw rate and the sideslip angle. The simulation results show the effectiveness of the proposed control method when the vehicle is subjected to different cornering steering manoeuvres such as change line and J-turn under different driving conditions (dry road and snow-covered).     

汽车ASR系统控制算法及其硬件在环仿真研究

研究了以节气门开度调节为主、制动干预为辅的汽车ASR控制算法.以常用节气门开度调节算法为基础,提出了自适应PID控制算法.制动干预采用传统的逻辑门限值法.为保证车辆加速的方向稳定性,研究了制动干预时的横摆力矩估算法,并根据横摆力矩限制制动压力来防止车辆的横摆运动.基于上述算法编写了程序代码写入自行开发的ECU,采用dSPACE硬件在环仿真系统进行验证.仿真结果表明控制算法行之有效.     

Modeling and control of an anti-lock brake and steering system for cooperative control on split-mu surfaces

The brake and steering systems in vehicles are the most effective actuators that directly affect the vehicle dynamics. In general, the brake system affects the longitudinal dynamics and the steering system affects the lateral dynamics; however, their effects are coupled when the vehicle is braking on a non-homogenous surface, such as a split-mu road. The yaw moment compensation of the steering control on a split-mu road is one of the basic functions of integrated or coordinated chassis control systems and has been demonstrated by several chassis suppliers. However, the disturbance yaw moment is generally compensated for using the yaw rate feedback or using wheel brake pressure measurement. Access to the wheel brake pressure through physical sensors is not cost effective; therefore, we modeled the hydraulic brake system to avoid using physical sensors and to estimate the brake pressure. The steering angle controller was designed to mitigate the non-symmetric braking force effect and to stabilize the yaw rate dynamics of the vehicle. An H-infinity design synthesis was used to take the system model and the estimation errors into account, and the designed controller was evaluated using vehicle tests.     

A curving ACC system with coordination control of longitudinal car-following and lateral stability

The paper presents a curving adaptive cruise control (ACC) system that is coordinated with a direct yaw-moment control (DYC) system and gives consideration to both longitudinal car-following capability and lateral stability on curved roads. A model including vehicle longitudinal and lateral dynamics is built first, which is as discrete as the predictive model of the system controller. Then, a cost function is determined to reflect the contradictions between vehicle longitudinal and lateral dynamics. Meanwhile, some I/O constraints are formulated with a driver permissible longitudinal car-following range and the road adhesion condition. After that, desired longitudinal acceleration and desired yaw moment are obtained by a linear matrix inequality based robust constrained state feedback method. Finally, driver-in-the-loop tests on a driving simulator are conducted and the results show that the developed control system provides significant benefits in weakening the impact of DYC on ACC longitudinal car-following capability while also improving lateral stability.     

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.