大型客车防抱死制动系统工作剖析

详细叙述了大型客车防抱死系统各部分的组成、安装、功能和参数,并给出防抱死制动系统的电路原理图及故障代码.     

从ABS到ASR、ESP看汽车动力性与制动性

介绍了ABS(防抱死制动系统)、ASR(驱动防滑系统)、ESP(电控行驶平稳系统)的功能和性能,分析了动力性、制动性与汽车行驶性能的关系.     

汽车制动能量回收系统的节能分析

文章分析了汽车制动时能量的回收与利用技术,提出了利用飞轮来储存汽车制动的能量,当汽车制动时,把汽车制动的能量转变为液压能,并将液压能转变为飞轮的机械能储存起来,在汽车起步或加速时再利用。建立了汽车定压源能量回收系统(以下简称CPS)模型,在给定制动工况的条件下,利用Matlab/Simulink工具对车辆"长安之星"SC6350的CPS进行了仿真研究,仿真结果表明,汽车CPS定压源能量回收系统的节能效果是明显的。     

PID在汽车防抱死系统中的仿真应用

本文通过数学建模建立了车辆动力学模型、滑移率模型、制动系统模型、轮胎模型、PID控制器等主要模型,然后建立了基于二轮车辆无ABS系统仿真模型、有PID控制器仿真模型,接着分别在高附着系数路面、低附着系数路面和对接路面上进行仿真试验。将三种不同车辆系统的制动时间、制动距离以及车轮是否抱死等性能参数进行对比分析,结果表明无ABS系统的车辆无论在哪种路况下制动,车轮均很快抱死,且制动时间最长,制动距离最大;有PID控制器的车辆可以使车轮滑移率在较短的时间内稳定在最佳滑移率处,避免了车轮抱死,制动距离与制动时间相比无ABS车辆都有明显改善。     

基于PID控制的汽车制动系统仿真分析

文章通过汽车理论和制动性性能的分析,建立包含汽车运动、车轮运动和车轮纵向摩擦力的汽车动力学模型。利用Matlab/Simulink仿真软件,建立汽车模型、制动器模型、轮胎模型和PID控制系统仿真分析模型,进行控制系统的仿真分析。并利用PID控制器以滑移率为控制目标,制动距离和制动时间为主要输出量,仿真研究汽车在不同的路面条件下的控制效果。仿真结果表明PID控制,在不同的路面条件下均可实现对车辆性能的有效控制。研究结果为提高汽车的制动安全性,提供了有益的参考。     

桥梁施工中液压滑模施工技术的应用分析

在桥梁施工阶段,为解决液压滑模提升不到位、操作方法不科学引起的工程质量问题,结合某桥梁项目实例,对液压滑模施工技术要点进行分析,提出液压滑模工艺方案,明确液压滑模施工方法。研究表明,按照规范执行系统安装与操作,能提升桥梁施工进度与质量。     

基于ADAMS的汽车驱动桥壳动力学仿真分析

利用机械系统动力学仿真软件ADAMS,建立了矿用汽车驱动桥非线性振动系统的虚拟样机模型,采用动力学仿真的方法,计算桥壳在不平路面激励下所受到的动载荷,仿真结果可为桥壳的有限元动态分析与设计提供载荷方面的准备。     

轨道交通ATS仿真运行框架的设计与实现

在分析轨道交通ATS系统特性的基础上,选用面向对象的仿真建模框架和逻辑控制框架,结合城市轨道交通的运行特点,对ATS仿真系统的时间控制和对象控制进行明确定义,克服传统仿真控制逻辑发散于描述模型过程代码中的缺点,介绍了一种面向对象的ATS仿真系统运行框架的设计和实现方法,为ATS仿真系统提供新的思路和方案。     

轨道交通列车制动系统离散性造成ATO停车精度问题的分析及解决

轨道交通信号系统采用基于通信的列车自动控制系统,列车根据实时的移动授权距离进行ATO驾驶。介绍了从列车制动系统时间和离散性的角度,对具有不同离散性的列车制动系统由于同一ATO控制命令响应偏差造成的ATO停车精度不准的问题,分析了ATO模式停车精度的优化设计,并在实际运营线路上进行了测试验证。测试结果表明,当遵守设计约束和改变ATO相关参数时,列车的停车精度得到了较大的提高。     

滑模施工方法在高桥墩施工中的应用

本文在介绍滑模系统构造设计的基础上.分析了滑模拼装及施工工艺,提出了滑模施工防偏、纠偏措施,对顶杆弯曲、天气原因导致停工等施工中常见问题提出了处理方法.为滑模施工方法在高桥墩施工中的推广应用提供参考.     

Designing a sliding mode controller for slip control of antilock brake systems

Antilock brake system (ABS) has been designed to achieve maximum negative acceleration by preventing the wheels from locking. Research shows that the friction between road and tire is a nonlinear function of wheel slip. Therefore, maximum negative acceleration can be achieved by designing a suitable control system for wheel slip regulation at its optimum value. Since there is a lot of nonlinearity and uncertainty (uncertainty in mass and center of gravity of the vehicle and road condition) in vehicle dynamics, a robust control method should be used. In this research, a sliding mode controller for wheel slip control has been designed based on a two-axle vehicle model. Important considered parameters for vehicle dynamic include two separated brake torques for front and rear wheels as well as longitudinal weight transfer caused by the acceleration or deceleration. One of the common problems in sliding mode control is chattering phenomenon. In this paper, primary controller design has been improved using integral switching surface to reduce chattering effects. Simulation results show the success of integral switching surface in elimination of chattering side effects and by high performance of this controller. At the end, the performance of the designed controller has been compared with three of the prevalent papers results to determine the performance of sliding mode control integrated with integral switching surface.     

Optimization of nonlinear control strategy for anti-lock braking system with improvement of vehicle directional stability on split-μ roads

In a hard braking on a split-μ road, the achievement of shorter stopping distance while maintaining the vehicle in the straight line are of great importance. In this paper, to achieve these conflicting aims, an optimal nonlinear algorithm based on the prediction of vehicle responses is presented to distribute the wheel braking forces properly. The base of this algorithm is reducing the maximum achievable braking forces of one side wheels, as low as possible, so that the minimum stabilizing yaw moment is produced. The optimal property of the proposed control method makes it possible to get a trade-off between the shorter stopping distance and the less deviation of the vehicle heading from the straight line. The special case of this algorithm leads to the conventional anti-lock braking system (ABS) which generates the maximum braking forces for all wheels to attain the minimum stopping distance. However, the ABS cannot control the vehicle directional stability directly. The simulation results carried out using a nonlinear 8-DOF vehicle model demonstrate that the designed control system has a suitable performance to attain the desired purposes compared with the convectional ABS.     

A hybrid model predictive control for traffic flow stabilization and pollution reduction of freeways

In this work a control system is developed and analyzed for the suppression of moving jamwaves and the reduction of pollutant concentrations near motorways. The system is based on the second-order macroscopic freeway traffic model METANET, joined by an emission dispersion model, introduced in a previous work of the authors. For the control tasks dedicated controllers are designed, both using the nonlinear model predictive control method. The control objectives require a distinction in the utilized control measures, thus different controllers are designed and used in predefined control modes. The first mode of the controller is responsible for keeping pollutant concentrations below prescribed limits under stable conditions. The second mode of the controller is working in case of a shockwave threat, aiming for traffic stabilization. Between the control modes switching is based on an appropriate rule set that satisfies the stability of the controlled system. The hybrid controller structure is realized by a finite automata. A complex case study is presented for the evaluation of the suggested controller.     

Surface prediction and control algorithms for anti-lock brake system

Anti-lock brake system (ABS) has been designed to achieve maximum deceleration by preventing the wheels from locking. The friction coefficient between tyre and road is a nonlinear function of slip ratio and varies for different road surfaces. In this paper, methods have been developed to predict these different surfaces and accordingly control the wheel slip to achieve maximum friction coefficient for different road surfaces. The surface prediction and control methods are based on a half car model to simulate high speed braking performance. The prediction methods have been compared with the results available in the literature. The results show the advantage of ABS with surface prediction as compared to ABS without proper surface identification. Finally, the performance of the controller developed in this paper has been compared with four different ABS control algorithms reported in the literature. The accuracy of prediction by the proposed methods is very high with error in prediction in a range of 0.17-2.4%. The stopping distance is reduced by more than 3% as a result of prediction for all surfaces.     

A dynamic automated lane change maneuver based on vehicle-to-vehicle communication

Automated driving is gaining increasing amounts of attention from both industry and academic communities because it is regarded as the most promising technology for improving road safety in the future. The ability to make an automated lane change is one of the most important parts of automated driving. However, there has been little research into automated lane change maneuvers, and current research has not identified a way to avoid potential collisions during lane changes, which result from the state variations of the other vehicles. One important reason is that the lane change vehicle cannot acquire accurate information regarding the other vehicles, especially the vehicles in the adjacent lane. However, vehicle-to-vehicle communication has the advantage of providing more information, and this information is more accurate than that obtained from other sensors, such as radars and lasers. Therefore, we propose a dynamic automated lane change maneuver based on vehicle-to-vehicle communication to accomplish an automated lane change and eliminate potential collisions during the lane change process. The key technologies for this maneuver are trajectory planning and trajectory tracking. Trajectory planning calculates a reference trajectory satisfying the demands of safety, comfort and traffic efficiency and updates it to avoid potential collisions until the lane change is complete. The trajectory planning method converts the planning problem into a constrained optimization problem using the lane change time and distance. This method is capable of planning a reference trajectory for a normal lane change, an emergency lane change and a change back to the original lane. A trajectory-tracking controller based on sliding mode control calculates the control inputs to make the host vehicle travel along the reference trajectory. Finally, simulations and experiments using a driving simulator are conducted. They demonstrate that the proposed dynamic automated lane change maneuver can avoid potential collisions during the lane change process effectively.     

Multi-scale perimeter control approach in a connected-vehicle environment

This paper proposes a novel approach to integrate optimal control of perimeter intersections (i.e. to minimize local delay) into the perimeter control scheme (i.e. to optimize traffic performance at the network level). This is a complex control problem rarely explored in the literature. In particular, modeling the interaction between the network level control and the local level control has not been fully considered. Utilizing the Macroscopic Fundamental Diagram (MFD) as the traffic performance indicator, we formulate a dynamic system model, and design a Model Predictive Control (MPC) based controller coupling two competing control objectives and optimizing the performance at the local and the network level as a whole. To solve this highly non-linear optimization problem, we employ an approximation framework, enabling the optimal solution of this large-scale problem to be feasible and efficient. Numerical analysis shows that by applying the proposed controller, the protected network can operate around the desired state as expressed by the MFD, while the total delay at the perimeter is minimized as well. Moreover, the paper sheds light on the robustness of the proposed controller. This multi-scale hybrid controller is further extended to a stochastic MPC scheme, where connected vehicles (CV) serve as the only data source. Hence, low penetration rates of CVs lead to strong noises in the controller. This is a first attempt to develop a network-level traffic control methodology by using the emerging CV technology. We consider the stochasticity in traffic state estimation and the shape of the MFD. Simulation analysis demonstrates the robustness of the proposed stochastic controller, showing that efficient controllers can indeed be designed with this newly-spread vehicle technology even in the absence of other data collection schemes (e.g. loop detectors).     

System energy optimisation strategies for metros with regeneration

Energy and environmental sustainability in transportation are becoming ever more important. In Europe, the transportation sector is responsible for about 30% of the final end use of energy. Electrified railway systems play an important role in contributing to the reduction of energy usage and CO₂ emissions compared with other transport modes. For metro-transit systems with frequently motoring and braking trains, the effective use of regenerated braking energy is a significant way to reduce the net energy consumption. Although eco-driving strategies have been studied for some time, a comprehensive understanding of how regeneration affects the overall system energy consumption has not been developed. This paper proposes a multi-train traction power network modelling method to determine the system energy flow of the railway system with regenerating braking trains. The initial results show that minimising traction energy use is not the same as minimising the system energy usage in a metro system. An integrated optimisation method is proposed to solve the system energy-saving problem, which takes train movement and electrical power flow into consideration. The results of a study of the Beijing Yizhuang metro line indicate that optimised operation could reduce the energy consumption at the substations by nearly 38.6% compared to that used with the existing ATO operation.     

Robust constrained control of uncertain macroscopic fundamental diagram networks

This paper considers modeling and control of uncertain Macroscopic Fundamental Diagram (MFD) systems for multiple-region networks. First, the nonlinear vehicle conservation equations based on MFD dynamics, presented in earlier publications, are transformed to linear equations with parameter uncertainties. The parameter uncertainties include the destination decomposition fractions, that are difficult to estimate in reality. Then, the uncertain linear model is utilized to design a robust feedback controller by an interpolation-based approach. This approach (i) guarantees robustness against all parameter uncertainties, (ii) handle control and state constraints, and (iii) present a computationally cheap solution. The main idea is to interpolate between (i) a stabilizing outer controller that respects the control and state constraints and (ii) an inner robustly stable controller designed by any method. The robust control is further challenged to deal with different relative locations of reference accumulation points on the MFD diagrams. Numerical results for a two-region system show that the uncertain linear model can replace the nonlinear model for modeling and control. Moreover, the robust control law is presented as implicit and explicit solutions, where in the implicit case one linear programming (LP) problem is solved at each time instant, while in the explicit case, the control law is shown as a piecewise affine function of state. Finally, a comparison between the interpolating controller and other controllers in the literature is carried out. The results demonstrate the performance advantages from applying the robust interpolating controller.     

Nonlinear gating control for urban road traffic network using the network fundamental diagram

This work proposes a nonlinear model predictive controller for the urban gating problem. The system model is formalized based on a research on existing models of the network fundamental diagram and the perimeter control systems. For the existing models, modifications are suggested: additional state variables are allocated to describe the queue dynamics at the network gates. Using the extended model, a nonlinear model predictive controller is designed offering a ‘non‐greedy’ policy compared with previous, ‘greedy’ gating control designs. The greedy and non‐greedy nonlinear model predictive control (NMPC) controllers are compared with a greedy linear feedback proportional‐integral‐derivative (PID) controller in different traffic situations. The proposed non‐greedy NMPC controller outperforms the other two approaches in terms of travel distance performance and queue lengths. The performance results justify the consideration of queue lengths in dynamic modeling, and the use of NMPC approach for controller design. Copyright © 2014 John Wiley & Sons, Ltd.     

Freeway ramp control using fuzzy set theory for inexact reasoning

This paper presents a fuzzy controller for freeway ramp metering, which uses rules of the form: IF “freeway condition” THEN “control action.” The controller has been designed to consider varied levels of congestion, a downstream control area, changing occupancy levels, upstream flows, and a distributed detector array in its rule base. Through fuzzy implication, the inference of each rule is used to the degree to which the condition is true. Using a dynamic simulation model of conditions0fj at the San Francisco-Oakland Bay Bridge, the action of the fuzzy controller is compared to the existing “crisp” control scheme, and an idealized controller. Tests under a variety of scenarios with different incident locations and capacity reductions show that the fuzzy controller is able to extract 40 to 100% of the possible savings in passenger-hours. In general, the fuzzy algorithm displays smooth and rapid response to incidents, and significantly reduces the minute-miles of congestion.