Evaluation of antilock braking system with an integrated model of full vehicle system dynamics

Antilock braking system (ABS), traction control system, etc. are used in modern automobiles for enhanced safety and reliability. Autonomous ABS system can take over the traction control of the vehicle either completely or partially. An antilock braking system using an on–off control strategy to maintain the wheel slip within a predefined range is studied here. The controller design needs integration with the vehicle dynamics model. A single wheel or a bicycle vehicle model considers only constant normal loading on the wheels. On the other hand, a four wheel vehicle model that accounts for dynamic normal loading on the wheels and generates correct lateral forces is suitable for reliable brake system design. This paper describes an integrated vehicle braking system dynamics and control modeling procedure for a four wheel vehicle. The vehicle system comprises several energy domains. The interdisciplinary modeling technique called bond graph is used to integrate models in different energy domains and control systems. The bond graph model of the integrated vehicle dynamic system is developed in a modular and hierarchical modeling environment and is simulated to evaluate the performance of the ABS system under various operating conditions.     

汽车转向/防抱死制动协同控制

为了解决汽车转向过程中防抱死制动稳定性问题,提出一种新的协同控制系统.该协同控制结构由转向控制器和制动控制器组成.在转向控制中设计滑模鲁棒自适应控制器和横摆力矩控制器力求改善汽车动态响应,鲁棒自适应性和稳定性.此外定义协同误差,建立汽车协同误差模型并设计汽车防抱死制动鲁棒自适应控制系统.为了减少转向系统和制动系统之间的补偿控制律难以确定的困难,提出耦合误差补偿原理与同一给定控制相结合的新的耦合控制策略.最后用仿真结果验证所设计控制算法的有效性.     

Influences of braking system faults on vehicle dynamics

From a safety point of view, the braking system is, besides the driver, one of the key subsystems in a car. The driver, as an adaptive control system, might not notice small faults in the hydraulic part of the braking system and sooner or later critical braking situations, e.g. due to a brake-circuit failure, may occur. Most drivers are not capable of dealing with such critical situations. Therefore, this paper investigates the influence of faults in the braking system on the dynamic vehicle behavior and the steering inputs of the driver required to keep the vehicle on the desired course.     

汽车转向防抱死制动控制系统研究

为了研究解决车辆转向过程中防抱死制动稳定性问题,设计了一种由执行级、协调级组成的分层控制系统,在执行级,设计了基于遗传算法的汽车ABS最优滑模控制器;设计了基于遗传算法的汽车转向滑模控制器。在协调级,针对制动和转向两个子系统提出协调控制方案,给出具体协调策略。用仿真结果验证所设计控制算法的稳定性和有效性。     

无轨胶轮车全液压制动系统故障诊断研究

针对无轨胶轮车全液压制动系统长期工作在煤矿井下恶劣的环境中而易于发生故障的问题,提出了基于支持向量机的故障诊断方法。应用支持向量机回归估计算法建立全液压制动系统的故障预测辨识模型,将支持向量机模式分类算法应用于故障分离,并在Matlab环境下分别进行了故障检测与故障分离试验。试验结果表明,将支持向量机方法应用于无轨胶轮车全液压制动系统故障诊断中是完全可行的,可以有效提高故障检测效率和诊断准确率。     

Design of grey-verhulst sliding mode controller for antilock braking system

In this paper, an adaptive controller called Grey-Verhulst Sliding Mode Controller (GVSMC) is proposed for the laboratory Antilock Braking System (ABS). The developed Grey-Verhulst Model (GVM) does a better prediction of wheel slip than a simple Grey Model. The first order Sliding Mode Controller (SMC) maintains the wheel slip at the desired value. By combining the GVM and SMC, the resulting GVSMC controls the wheel slip at the desired optimum value at which the vehicle control, non-skidding and steerability are ensured during sudden braking. The proposed controller also reduces the stopping distance considerably. Simulation results show that the performance of the proposed GVSMC is better than the simple SMC and Grey SMC reported in literature earlier. Change in road conditions has also been considered.     

汽车转向/防抱死制动系统的自调节协调控制*

为了解决汽车转向过程中防抱死制动稳定性问题,提出一种新的协调控制系统,该协调控制结构由转向控制器和制动控制器组成。在转向控制中,设计基于主动前轮控制器和横摆力矩控制器力求改善汽车动态响应和稳定性;设计汽车防抱死制动控制系统。为了减少针对转向系统和制动系统之间的补偿控制律难以确定的困难,定义了协调误差,提出了基于耦合误差补偿原理与给定控制相结合的新的耦合控制策略,最后用仿真结果验证所设计控制算法的有效性。     

Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.     

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input–output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.     

Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control

The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.     

A command processor system with an integrated data base for flexible mechanical system dynamics

This paper presents a unified command processor system with an integrated data base for flexible mechanical system dynamics. The system is demonstrated using two well established finite element programs, ANSYS and NASTRAN, and two general purpose flexible system dynamics programs, DADS and DISCOS. The command processor system is based on the recently developed Integrated Analysis Capability (IAC). Using the IAC, well established analysis tools can be used to analyze different aspects of physical behavior that are modelled by a variety of mathematical concepts. The paper (1) describes the requirements for an interdisciplinary command and data base management system, (2) explains the process of incorporating finite element and system dynamic programs into the integrated analysis system, and (3) develops a unified data base for flexible system dynamics. The modular structure and the ease with which new modules can be added to the system make it suitable for analysis of a wide spectrum of flexible dynamics problems.     

液压混合动力履带车辆联合制动模糊控制

针对液压混合动力履带车辆联合制动系统,为了实现制动过程平稳性,提出了基于制动力分配原则的模糊控制策略.首先在MATLAB中建立了能量再生制动系统和机械制动系统以及车辆动力学仿真模型,然后设计了以制动力分配系数为控制变量的联合制动模糊控制器,给出了模糊控制规则,建立了控制系统仿真模型,并在不同制动强度条件下对车辆制动过程进行仿真.仿真结果表明,联合制动模糊控制系统能够有效回收制动能量,同时与PID控制相比明显改善和提高了履带车辆制动过程稳定性.     

CBA-neural network control of a non-linear full vehicle model

In this paper, the dynamic behavior of a non-linear eight degrees of freedom vehicle model having active suspensions and passenger seat controlled by a neural network (NN) controller is examined. A robust NN structure is established by using principle design data from the Matlab diagrams of system functions. In the NN structure, Classic Back-Propagation Algorithm (CBA) is employed. The user inputs a set of x₁ − x₁₆ while the output from the NN consists of f₁ − f₁₆ non-linear functions. Further, the Permanent Magnet Synchronous Motor (PMSM) controller is also determined using the same NN structure. According to various tests of the NN structure it is demonstrated that the model is able to give highly sensitive outputs for vibration condition, even using a more restricted input data set. The non-linearity occurs due to dry friction on the dampers. The vehicle body and the passenger seat using PMSM are fully controlled at the same time. The time responses of the non-linear vehicle model due to road disturbance and the frequency responses are obtained. Finally, uncontrolled and controlled cases are compared. It is seen that seat vibrations of a non-linear full vehicle model are controlled by NN based system exactly.     

TDOA-DR组合车辆导航定位系统研究

针对自主导航小车在区域行驶中的定位问题,研究了TDOA-DR组合导航定位算法。通过对基于到达时间差(TDOA)的双曲线导航原理的分析,采用了基于超声波传感器的多基站双曲线定位系统。并在此基础上,对TDOA-DR组合导航系统的结构和算法进行研究和仿真分析。仿真结果表明:TDOA-DR组合导航系统能够在区域内提供厘米级精度的连续车辆位置信息,满足区域高精度定位的需求。     

无人机全电式自主刹车系统滑模极值搜索控制

常规主动刹车系统采用在线辨识跑道特征的算法,但仍需依赖摩擦模型先验知识,难以应对复杂跑道工况.为克服上述问题,提出一种滑模极值搜索控制策略并应用于无人机全电式自主刹车系统.考虑电动作动机构非线性特性,建立系统的状态空间模型并合理简化为严格反馈形式,采用超扭曲算法估计结合系数的梯度,结合反馈线性化控制律得到刹车压力参考值,证明此控制作用下可实现对未知最优滑移率的渐近跟踪.采用反演控制的思想设计无抖振滑模控制器实现对参考刹车压力的跟踪.利用Lyapunov方法获得系统的渐近稳定性条件并分析控制参数对系统的影响.半实物仿真试验结果表明控制策略的有效性.     

Set-membership LPV model identification of vehicle lateral dynamics

Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output LPV models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of maneuvers is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameter measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed.     

Forward vehicle collision mitigation by braking system based on artificial bee colony algorithm

Decision-making is a crucial step in vehicles’ safety systems, which determines the right time for the system to intervene and to take the required action, depending on the current situation of the vehicle. The warning/intervention system becomes active when the driving situation considered unpleasant while still giving the control to the driver. Taking the action of warning, braking and determining the best time for this intervention are important decisions in collision mitigation systems. In this work, a novel system is designed based on artificial bee colony algorithm to enhance the decision-making in such systems. This work concentrates on collision mitigation by braking systems only, for front-to-rear accidents. This paper studies cases when the hosting vehicle approaches moving and stationary objects. This work is simulated, and the results are obtained and analyzed which proved the contribution to this work in reducing the collision speed and the stopping distance, in comparison with previous related works.