A Pitch-Plane Model of a Vehicle with Controlled Suspension

A vehicle model incorporating front and rear wheel suspensions and seat suspension is presented. The suspension control includes algorithms to provide both dynamic and steady state (levelling) control. Vehicle response to (a) vertical inputs due to ground disturbances at the wheels and (b) longitudinal inputs due to the inertial forces during braking and accelerating, are investigated. It is shown that the static (self-levelling) control causes a slight deterioration in dynamic performance. The active ride control produces improvements of ride comfort under dynamic conditions compared to an equivalent passively suspended vehicle. In steady state the proposed control eliminates the error heave of the body caused by tilting of the vehicle with active suspension.     

Fueling Control of Spark Ignition Engines

Presented in this paper is an adaptive, model based, fueling control system for spark ignition-internal combustion engines. Since the fueling control system is model based, the engine maps currently used in engine fueling control are eliminated. This proposed fueling control system is modular and can therefore accommodate changes in the engine sensor set such as replacing the mass-air flow sensor with a manifold air pressure sensor. The fueling algorithm can operate with either a switching type O 2 sensor or a linear O 2 sensor. The fueling control system is also parceled into steady state fueling compensation and transient fueling compensation. This feature provides the distinction between fueling control adaptation for transient fueling and steady state fueling. The steady state fueling compensation utilizes a feedforward controller which determines the necessary fuel pulsewidth after a throttle transient to achieve stoichiometry. This feedforward controller is comprised of two nonlinear models capturing the steady state characteristics of the fueling process. These models are identified from an input-output testing procedure where the inputs are fuel pulsewidth and mass-air flow signal and the output is a lambda signal. These models are adapted via a recursive least squares method to accommodate product variability, engine aging, and changes in the operating environment. The transient fueling compensation also utilizes a feedforward controller that captures the essential dynamic characteristics of the transient fueling operation. This controller is measured using a frequency domain system identification approach. This proposed fueling control system is demonstrated on a Ford 4.6L V-8 fuel injected engine.     

Fueling Control of Spark Ignition Engines

Presented in this paper is an adaptive, model based, fueling control system for spark ignition-internal combustion engines. Since the fueling control system is model based, the engine maps currently used in engine fueling control are eliminated. This proposed fueling control system is modular and can therefore accommodate changes in the engine sensor set such as replacing the mass-air flow sensor with a manifold air pressure sensor. The fueling algorithm can operate with either a switching type O 2 sensor or a linear O 2 sensor. The fueling control system is also parceled into steady state fueling compensation and transient fueling compensation. This feature provides the distinction between fueling control adaptation for transient fueling and steady state fueling. The steady state fueling compensation utilizes a feedforward controller which determines the necessary fuel pulsewidth after a throttle transient to achieve stoichiometry. This feedforward controller is comprised of two nonlinear models capturing the steady state characteristics of the fueling process. These models are identified from an input-output testing procedure where the inputs are fuel pulsewidth and mass-air flow signal and the output is a lambda signal. These models are adapted via a recursive least squares method to accommodate product variability, engine aging, and changes in the operating environment. The transient fueling compensation also utilizes a feedforward controller that captures the essential dynamic characteristics of the transient fueling operation. This controller is measured using a frequency domain system identification approach. This proposed fueling control system is demonstrated on a Ford 4.6L V-8 fuel injected engine.     

基于毫米波雷达组群的全域车辆轨迹检测技术方法

高精度车辆轨迹数据对于高速公路交通管理和智慧服务具有非常重要的研究及应用价值，然而现有的车辆轨迹感知技术难以获得全域全时车辆轨迹数据。为此，提出一种基于毫米波雷达的全域车辆轨迹跟踪技术方法，该方法包括：雷达原始数据获取及适配、轨迹数据清洗及降噪、道路线形感知及还原、车辆轨迹匹配及拼接。其中，雷达原始数据获取及适配通过构建雷达帧数据适配表将雷达数据格式标准化，并通过构建的轨迹可信度评价指标K，剔除镜像车辆轨迹数据，进而基于历史行车轨迹的统计学特征，采用聚类方法还原道路线形，最终通过雷达群组间车辆轨迹特征分析及匹配拼接，实现设备内部及跨设备对车辆轨迹的持续跟踪。利用载波相位差分技术(Real-time Kinematic, RTK)和基于无人机航拍视频定位技术分别对单车及多车轨迹跟踪精度进行检验。研究结果表明：在单目标跟踪状态下，系统的纬度偏差均值为-0.284 m，经度偏差均值为-0.352 m，纬度误差均值为0.712 m，经度误差均值为0.539 m；在多目标跟踪状态下，系统丢车率约为8%，轨迹定位与真实位置偏差均值为0.990 m，具备良好的轨迹跟踪精度。该方法为未来从更加宏观的范围内研究个体驾驶行为风险转移分析、微观水平的驾驶风险的时空演化提供了数据支撑。     

Using GPS with a model-based estimator to estimate critical vehicle states

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.     

汽油机点火正时微处理机自适应控制系统的研究

本文介绍了一种利用微处理机自适应控制汽油机最佳点火正时的系统。该系统由一单板微处理机和一套全晶体管电子点火装置组成,反馈信号由汽油机转速传感器检取。汽油机稳定工况的台架试验和整车道路试验表明:用这种自适应控制点火系统与用机械分电器装置相比,汽油机的输出功率增大,油耗率下降,整车的动力性和燃油经济性得到提高。     

面向低速清扫车的信息融合车辆跟踪方法

交通参与者运动的准确跟踪与预测对智能车行为决策的有效性至关重要。传统运动目标的跟踪系统多采用单一传感器,难以保证数据的精度与可信度。为提高系统的鲁棒性与可靠性,设计一种融合毫米波雷达和相机的目标跟踪方案,该方案针对多目标的特征级信息进行融合。首先,考虑低速行驶的自动驾驶清扫车所处环境杂波较多,方案选择基于IMM/JPDA的多目标跟踪方法估计局部航迹。为降低JPDA数据关联的计算复杂度,结合基于马氏距离构造的椭圆关联门和基于车辆非完整性约束构造的扇形关联门,实现关联门的自适应调整,减少关联杂波的干扰。其次,结合传感器的配置与特性,对目标的航迹状态进行空间对准和时间对准,按照航迹点间的欧氏距离和互协方差选择融合模式,进行局部航迹融合。最后,为验证多目标跟踪和航迹融合方法的有效性与实用性,分别设计基于MATLAB/PreScan环境的仿真试验和基于智能清扫车平台的实车试验。研究结果表明：在横、纵方向上,融合后的系统状态都比单一传感器的估计状态更为准确,融合结果对单一传感器的估计误差有35%以上的提升;实车试验证明,该方案能有效融合ESR毫米波雷达和Mobileye单目前视相机的状态估计信息,能基本正确地跟踪目标和估计航迹;融合状态的横、纵向误差都在可接受范围以内,且融合状态比单一传感器的估计波动更小。     

Modified lateral control of an autonomous vehicle by look-ahead and look-down sensing

This paper presents a modified lateral control method for an autonomous vehicle with both look-ahead and look-down sensing systems. To cope with sensor noise and modeling uncertainty in the lateral control of the vehicle, a modified LMI-based H lateral controller was proposed, which uses the look-ahead information of the lateral offset error measured at the front of vehicle and the look-down information of the vehicle yaw angle error between the reference lane and the centerline of the vehicle. To verify the safety and the performance of the lateral control, a scaled-down vehicle was developed, and the positioning of the vehicle was estimated with USAT. The proposed controller, which uses both look-ahead and look-down information, was tested for lane changing and reference lane tracking with both simulation and experiment. The simulation and experimental results show that the proposed controller has better tracking and handling performance compared with a controller that uses only the look-ahead information of the target heading angle error.     

Nonlinear Model-Based Multivariable Control for Air & Charging System of Diesel Engine with Short and Long Route EGR Valves

The objective of this study is to investigate a nonlinear model-based multivariable (MIMO, Multi Input Multi Output) technique to decouple actuators interaction and to reduce the calibration effort, while increasing control performances, above all in transient conditions, and robustness with respect to model uncertainties and system parameter variations. The presented control technique is based on the development of a nonlinear dynamical physical model of the diesel air and charging system. Feedback Linearization control is then applied to decouple actuators’ interactions and compensate for nonlinearities. A new set of virtual inputs are defined inverting the system differential equations. Relation among the new virtual inputs and the outputs is purely linear and decoupled, meaning that each virtual input affects linearly only one output. Moreover, a linear control block is added to guarantee transient and steady state performances and closed loop robustness. The proposed control approach has been validated through small diesel engine dyno and vehicle activities. Transient test bench maneuvers show that the control is able to coordinate the actuators in order to fulfill the targets and to guarantee similar performances in different operating points. In addition the robustness to environmental changes has been demonstrated by vehicle tests at different ambient conditions.     

Measurements of Path and Other Parameters in Motor Vehicle Dynamics Tests and Their Errors

This paper discusses two equipment layouts for measuring path data, vehicle sideslip angles and tire slip angles by using a gyrosystem, a vehicle speed-measuring unit with contact or non-contact speed sensor(s), and a computer. Measurement errors are analyzed in detail. A method for eliminating the effect of vehicle roll on measurement is given. Some relations between measurement errors and instrument errors are derived using vector analysis. A practical and effective error correction method for vehicle path measurements is developed.     

混合动力汽车汽油机电子节气门模糊智能PID控制

应用英飞凌新一代车用嵌入式控制芯片XC164,开发了基于模糊智能积分PID复合控制算法的混合动力汽车汽油机电子节气门控制系统,为混合动力汽车主控制器和发动机控制之间提供了控制接口。通过对混合动力轿车进行的实际行驶中频繁急加速、急减速过程中电子节气门目标开度和实际控制开度的对比试验,验证了电子节气门控制响应速度快、稳态误差小及控制系统软硬件设计满足混合动力总成对发动机控制的要求。     

车-车协同下无人驾驶车辆的换道汇入控制方法

多车协同驾驶是智能车路系统领域的研究热点之一,可有效降低道路交通控制管理的复杂程度,减少环境污染的同时保障道路交通安全。基于多车协同驾驶控制结构,提出了一种无人驾驶车辆换道汇入的驾驶模型及策略,系统分析了多车协同运行状态的稳定条件。在综合分析无人驾驶车辆换道汇入的协作准则、安全性评估后,基于高阶多项式方法,结合车辆运行特性,通过引入乘坐舒适性的指标函数,设计得到无人驾驶车辆换道汇入的有效运动轨迹。通过研究汇入车辆与车队中汇入点前、后各车辆的运动关系,详细分析车辆发生碰撞的类型和影响因素,给出避免碰撞的条件准则,从而确保无人驾驶车辆汇入过程中多车行驶的安全性和稳定性。基于车辆运动学建立车辆位置误差模型,结合系统大范围渐进稳定的条件,选取线速度和角速度作为输入,应用李雅普诺夫稳定性理论和Backstepping非线性控制算法,设计了无人驾驶车辆换道汇入后的路径跟踪控制器。仿真试验和实车试验结果表明：所设计的换道汇入路径是可行、安全的,控制器具有良好的跟踪效果,纵向和横向的距离误差在15 cm以内,方向偏差的相对误差在10%以内。研究结果为智能车路系统中的多车状态变迁与协同驾驶研究提供了参考,可服务于未来道路交通安全设计和评价。     

Optimal Linear Active Suspensions with Multivariable Integral Control

In this paper, an optimal suspension system is derived for a quarter-car model using multivariable integral control. The suspension system features two parts. The first part is an integral control acting on suspension deflection to ensure zero steady-sate offset due to body and maneuvering forces as well as road inputs. The second is a proportional control operating on the vehicle system states for vibration control and performance improvement. The optimal ride performance of the active suspensions based on linear full-state feedback control laws with and without integral control together with the performance of passive suspensions are compared.     

Literature review and fundamental approaches for vehicle and tire state estimation*

ABSTRACT

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.     

Terrain-based road vehicle localisation using particle filters

This work develops a particle filter algorithm to localise a vehicle in the direction of travel without the use of GPS. The inputs to the algorithm include a terrain map of road grade, pitch measurements from an in-vehicle pitch sensor, and wheel odometry. Simulations and experiments at The Thomas D. Larson Transportation Institute test track are used to demonstrate the algorithm, observe the speed of convergence, and to determine key parameters for practical implementation. The results indicate that the method can quickly localise a vehicle with 1 m accuracy or better. Experiments over 5 km along Highway 322 in State College, Pennsylvania, were also used to demonstrate the algorithm.     

DISCUSSION NOTE on Karnopp's article “Active Damping in Road Vehicle Suspension Systems”

SUMMARY

In this paper, an optimal suspension system is derived for a quarter-car model using multivariable integral control. The suspension system features two parts. The first part is an integral control acting on suspension deflection to ensure zero steady-sate offset due to body and maneuvering forces as well as road inputs. The second is a proportional control operating on the vehicle system states for vibration control and performance improvement. The optimal ride performance of the active suspensions based on linear full-state feedback control laws with and without integral control together with the performance of passive suspensions are compared.     

Optimal Linear Active Suspensions with Derivative Constraints and Output Feedback Control

In optimally controlled active suspensions with either full or incomplete state feedback there is tradeoff between system performance and overall stiffness. It is sought to remove this limitation by incorporating integral action which results in a system with infinite stiffness towards static loading, but which is soft with respect to road inputs. The system is also able to eliminate the steady state deflections due to step and (potentially) ramp type inputs at the wheel. Optimality is retained at the cost only of a derivative constraint and the system can be physically realised using output feedback control.     

电动轮汽车车速与道路坡度估计

针对电动轮汽车车速与道路坡度估计问题,本文中基于纵向非线性动力学方程设计1阶扩张状态观测器对车速与坡度进行联合估计,分析了估计稳态误差;同时,采用带遗忘因子的递归最小二乘估计算法分离加速度传感器信号中的坡度信息,并设置了比例系数来融合两类坡度信息,最终得到道路坡度估计值。搭建MATLAB/Simulink-Carsim联合仿真平台进行变坡度路面仿真,并在实际坡道路面完成实车测试。仿真与试验结果表明,所提出的方法简单、可行。     

An Extended Adaptive Kalman Filter for Real-time State Estimation of Vehicle Handling Dynamics

This paper considers a method for estimating vehicle handling dynamic states in real-time, using a reduced sensor set; the information is essential for vehicle handling stability control and is also valuable in chassis design evaluation. An extended (nonlinear) Kalman filter is designed to estimate the rapidly varying handling state vector. This employs a low order (4 DOF) handling model which is augmented to include adaptive states (cornering stiffnesses) to compensate for tyre force nonlinearities. The adaptation is driven by steer-induced variations in the longitudinal vehicle acceleration. The observer is compared with an equivalent linear, model-invariant Kalman filter. Both filters are designed and tested against data from a high order source model which simulates six degrees of freedom for the vehicle body, and employs a combined-slip Pacejka tyre model. A performance comparison is presented, which shows promising results for the extended filter, given a sensor set comprising three accelerometers only. The study also presents an insight into the effect of correlated error sources in this application, and it concludes with a discussion of the new observer's practical viability.     

Low cost design of parallel parking assist system based on an ultrasonic sensor

This paper presents a low cost design and implementation of a parallel parking assist system (PPAS) based on ultrasonic sensors. Generally, a PPAS requires several types of sensors, such as an ultrasonic sensor, camera sensor, radar sensor and laser sensor for parking space detection. However, our proposed PPAS only requires two ultrasonic sensors on the front and lateral sides for parking space detection. Moreover, a steering angle sensor and wheel speed sensor installed in the vehicle are used to obtain vehicle position information for localization in ultrasonic range data. The hardware architecture of the PPAS based on an electronic control unit (ECU) module, sensor modules and a human machine interface (HMI) module was proposed. Moreover, the software architecture of the PPAS is based on system initialization, scheduling, recognition and a control algorithm. In particular, a novel sensor algorithm was proposed to minimize the vehicle corner error of the ultrasonic sensor. A prototype of the PPAS based on the proposed architecture was constructed. The experimental results demonstrate that the implemented prototype is robust and successfully performs parking space detection and automatic steering control. Finally, the low cost design and implementation of the PPAS was possible due to the cheap ultrasonic sensors, simple hardware design and low computational complexity of the proposed algorithm.