混合动力汽车总成控制器接口设计

汽车总成控制器是混合动力汽车的控制枢纽,介绍一个基于Intel 80C196KC的混合动力汽车总成控制器的硬件接口设计,系统包括存储器模块、A／D模块、D／A模块、I／O模块以及RS232串行通信接口的电路设计。单片机外部总线采用明动态切换方式,串行通信采用MAX232集成器件,实验结果表明,该控制器具有较高的数据处理性能和可靠性,硬件接口设计合理,响应速度快,满足实验台架的控制要求。     

功率硬件在环双馈风机仿真系统

基于模型设计开发的理念设计了功率硬件在环双馈风机仿真系统。该仿真系统由上位机、转速采集卡、变频器、异步电机硬件在环仿真实际风机,双馈电机采用模块式背靠背双PWM变流器控制。变流器采用双控制器控制,NI通用控制器进行PWM驱动控制,TI-28335 DSP辅助控制,NI控制器的程序由Simulink模型直接生成,辅助控制器运行普通控制算法、逻辑、软件保护,可根据实验需求配置两者的输出控制量。该系统能够完成各种复杂工况的研究,可以由Simulink模型直接生成控制器运行代码,能够快速实现变流器的复杂控制算法,提高科研效率,降低测试成本。     

民用飞机环控系统的仿真系统研究与实现

针对民用飞机环控系统的研究、开发和试验需要,讨论了环控仿真系统的功能需求与系统架构,提出了一种适用于ECS控制器开发全过程的硬件在回路仿真系统.仿真系统采用两台实时仿真机分别运行ECS控制器模型和ECS硬件模型,实现在研发前期确定环控系统动态性能指标和对ECS控制算法的设计与验证;并采用配线矩阵,可以在不改变布线的情况下随时接入真实试验件,实现在研发后期对ECS控制器实物的测试、系统故障模拟以及与真实试验件的联合试验.为ECS控制器的开发全过程提供了从算法到实物的研究、设计与验证的一体化仿真平台.     

燃料电池汽车整车控制器硬件在环实时仿真测试平台设计

利用Matlab/Somulink实时仿真环境、工业用数据板卡、CAN通讯设备,系统地设计了燃料电池汽车整车控制器硬件在环实时仿真测试平台。利用该平台可以对整车控制器硬件电气特性、底层软件平台和控制算法等进行测试,有效地加快整车控制器的开发进程。     

基于虚拟仪器的VCU自动测试系统设计

在纯电动汽车开发过程中,整车控制器的软件与硬件会频繁更新,为了快速匹配样件、提高测试效率、提升测试吞吐量,设计了通用自动化测试系统。该系统使用TestStand作为测试执行模块。在LabVIEW中开发了一套即时可用的测试动作库和驱动程序,动作库执行测试动作,通过TestStand引擎与驱动程序进行数据交互。设计了测试项目管理软件,覆盖VCU测试全过程并保证可追溯性。实际测试应用表明,该系统可以快速配置测试环境、生成可执行测试序列,能够高效地定位控制器缺陷,且运行稳定、通用性好。     

汽车ASR系统ECU开发及其硬件在环测试

主要设计和开发了 ASR 控制的核心部件——电子控制单元(ECU)并进行了测试。ECU 硬件主要包括 MCU 最小系统电路、SPI 通讯电路、轮速信号处理电路、执行机构驱动电路和 CAN 通讯电路等;设计了软件方案、编写了程序代码。把开发的 ECU 在以 dSPACE 为核心的硬件在环试验台进行了硬件在环测试。测试结果表明 ECU 能够实现驱动防滑控制功能,用硬件在环仿真方法开发电子控制单元有较大的优越性。     

PSAT软件的前向仿真方法

该文在介绍电动汽车前向仿真方法工作原理的基础上,分析了PSAT软件的仿真原理。PSAT的模型由驾驶员、控制器、部件控制单元和传动系统四个模块组成,论文以一具体的混合动力汽车模型为例,分析了四个模块的作用和仿真流程。它对PSAT软件的应用具有参考作用。     

机电作动器悬架硬件在环仿真测试平台研究

为了提高汽车的平顺性,提出一种机电作动器悬架,设计了机电作动器悬架硬件在环仿真测试平台。建立基于机电作动器的主动悬架数学模型与仿真模型,以嵌入式系统单片机为主处理器设计机电作动器悬架的控制器;在此基础上,以dSPACE为模型运行载体搭建机电作动器悬架硬件在环仿真测试平台;利用该测试平台进行了仿真试验。结果表明所研究的硬件在环仿真测试平台具备较好的硬件在环仿真功能,能够对机电作动器悬架性能、主动悬架控制算法进行验证与评价。     

基于视频暗箱的车道保持系统HIL测试研究

为解决实车的车道保持辅助系统(LKA)的高效测试的问题,搭建了基于预瞄驾驶员模型的车道保持控制算法,并提出了基于视频暗箱的车载前视摄像头和ADAS控制器双硬件在环测试方法。方法将摄像头集成在以NI PXI为核心的硬件在环实时仿真系统中,使用CarMaker软件进行车辆动力学和场景建模,通过视频暗箱采集虚拟场景的视频信号用于模拟实际道路情况,完成车道保持辅助系统虚拟仿真测试。结果表明,直线工况下方向盘转角在-1～1deg之间变化,控制器控制效果较为平稳。硬件在环与软件在环的测试结果平均误差在5%以下,表明上述方法对车道保持控制器有较好的测试精度。     

嵌入式系统软件模拟器设计

利用软/硬件协同设计的方法，将嵌入式系统设计采用软件模拟系统环境来开发，通过对CPU行为、内存、中断控制器和操作系统等模块的设计，把硬件系统设计和软件系统仿真相结合。是目前嵌入式系统设计的全新的方法。通过实例对软件模拟器进行验证，表明了这种方法的可行性和可靠性，为嵌入式系统的开发提供软件系统仿真的经验。     

燃料电池动力系统硬件在环仿真开发

建立了燃料电池硬件在环仿真系统平台，包括燃料电池动力系统各部件模型和控制系统底层软硬件平台，对多能源动力系统能量管理的不同控制算法进行了分析。硬件在环仿真系统在动力控制系统开发中得到了广泛的应用，并对恒压控制算法进行了台架试验，验证了硬件在环仿真系统的有效性。     

小型飞艇自驾仪硬件在回路仿真平台设计

设计小型飞艇自驾仪的硬件在回路仿真平台,包括建立基于嵌入式系统ARM9的飞控系统验证机,采用分层结构方式的导航与控制模块。同时构建小型飞艇的动力学、压控系统和传感器仿真模型,充分发挥硬件在回路仿真测试系统软硬件结合的特点,缩短研发周期,提高系统可靠性。仿真结果表明了该平台的有效性。     

FADEC硬件在回路测试系统设计

发动机全权限数字电子控制器(FADEC)对航空发动机进行实时在线控制,其控制效果直接影响发动机的安全运行.硬件在回路测试系统是FADEC设计与验证的重要手段之一.设计了一种FADEC硬件在回路测试系统,该系统包括发动机电子控制器(EEC)、接口模拟器、发动机和飞机模型及其显示系统.该系统可以实时模拟发动机和飞机的各种传感器、执行机构信号,对FADEC进行高效、全面的验证,主要包括控制规律、EEC的接口电路、自检测、容错和重构策略等.通过大量的试验测试表明,该系统能够实现实时、稳定、高效的闭环仿真,达到了验证控制算法和控制逻辑的要求.     

基于虚拟仪器的舵机半实物仿真系统研究

传统舵机控制算法的设计大多是通过数字仿真来实现的,数字仿真过程中舵机执行机构模型的不准确使得仿真结果与实际情况有较大的差距。提出一种基于虚拟仪器的舵机控制半实物仿真系统,该系统利用LabVIEW软件平台开发了舵机执行机构的控制算法仿真模块,并通过D/A设备将控制参数发送给舵机执行机构。结合高速A/D设备对舵机反馈信号进行采集,在软件中实现对舵机控制算法性能的实时分析。试验结果表明,该系统解决了数字仿真中舵机执行机构模型不准确的问题,为舵机开发过程中的控制算法设计仿真提供了更为灵活、高效的方法。     

模糊控制在摆缸位置伺服系统中的应用

由于摆动气缸的摩擦转矩、空气可压缩性、比例阀的压力特性等非线性因素的存在,要得到摆缸伺服系统精确的数学模型十分困难,而且气动系统易受周围环境因素的影响,当采用传统PID控制时,很难使系统长时间保持良好的控制效果,尤其是当有一定的外界干扰之后会导致系统工作不稳定.利用Matlab实时仿真模块的功能,把模糊自适应整定PID的控制算法引入到伺服系统的控制器设计中,搭建的半实物仿真模型,对系统进行半实物仿真实验,并通过不断地在线修改控制规则,提高控制器的控制性能,实验结果表明,模糊PID控制效果良好.     

Hardware-in-the-loop simulation for the design and testing of engine-control systems

For the design, implementation and testing of control systems hardware-in-the-loop (HIL) simulation is increasingly being required, where some of the control-loop components are real hardware, and some are simulated. Usually, a process is simulated because it is not available (simultaneous engineering), or because experiments with the real process are too costly or require too much time. The real-time requirements for such simulations depend on the time-scale of the process and the simulated components involved. This paper gives, first, an overview of the various kinds of real-time and HIL simulation. Then, two cases are considered. First, HIL simulation for relatively slow processes, like those in basic industries or heating systems, is discussed. Then, the HIL simulation of combustion engines is shown in detail. The required models are described. Comparisons between real-time simulations and measurements on real diesel engines and trucks are shown. The goal of the HIL system is to develop new control algorithms and to investigate the effect of faults, both in sensors and actuators, and the engine itself.     

Hardware-in-the-loop simulation for the design and verification of the control system of a series–parallel hybrid electric city-bus

Hybrid electric buses have been a promising technology to dramatically lower fuel consumption and carbon dioxide (CO₂) emission, while energy management strategy (EMS) is a critical technology to the improvements in fuel economy for hybrid electric vehicles (HEVs). In this paper, a suboptimal EMS is developed for the real-time control of a series–parallel hybrid electric bus. It is then investigated and verified in a hardware-in-the-loop (HIL) simulation system constructed on PT-LABCAR, a commercial real-time simulator. First, an optimal EMS is obtained via iterative dynamic programming (IDP) by defining a cost function over a specific drive cycle to minimize fuel consumption, as well as to achieve zero battery state-of-charge (SOC) change and to avoid frequent clutch operation. The IDP method can lower the computational burden and improve the accuracy. Second, the suboptimal EMS for real-time control is developed by constructing an Elman neural network (NN) based on the aforementioned optimal EMS, so the real-time suboptimal EMS can be used in the vehicle control unit (VCU) of the hybrid bus. The real VCU is investigated and verified utilizing a HIL simulator in a virtual forward-facing HEV environment consisting of vehicle, driver and driving environment. The simulation results demonstrate that the proposed real-time suboptimal EMS by the neural network can coordinate the overall hybrid powertrain of the hybrid bus to optimize fuel economy over different drive cycles, and the given drive cycles can be tracked while sustaining the battery SOC level.     

基于模型设计的辅助驾驶系统硬件在环测试平台设计

近年来,基于模型的开发方式(Model Based Development,MBD)逐渐成为汽车软件系统开发的主流方式,而硬件在环(Hardware-In-the-Loop,HIL)测试是实现MBD的关键步骤.现基于MBD,在MATLAB/Simulink环境中,对针对ADAS算法的自动化硬件在环仿真测试进行研究,通过...     

Time Delay Compensation for Hardware-in-the-loop Simulation of a Turbojet Engine Fuel Control Unit Using Neural NARX Smith Predictor

Hardware-in-the-loop (HIL) simulation is an effective technique that is used for development and testing of control systems while some of the control loop components are simulated in a proper environment and the other components are real hardware. In a conventional HIL simulation, the hardware is an electronic control unit which electronic control signals are communicated between the hardware and the software. But, HIL simulation of a mechanical component requires additional transfer systems to connect the software and hardware. The HIL simulation can achieve unstable behavior or inaccurate results due to unwanted time-delay dynamic of the transfer system. This paper presents the use of Smith predictor for time-delay compensation of transfer system in the HIL simulation of an electro-hydraulic fuel control unit (FCU) for a turbojet engine. A nonlinear auto regressive with exogenous input (NARX) neural network model is used for modeling and predicting the FCU behavior. The neural model is trained by Levenberg-Marquardt algorithm and the training and validation sets are generated using the amplitude modulated pseudo random binary sequence (APRBS). The consistency of the experimental real-time simulation and off-line simulation shows the applicability of the presented method for mitigating the effect of unwanted dynamic of the transfer system in the HIL simulation.

     

A DDDAMS-based planning and control framework for surveillance and crowd control via UAVs and UGVs

A dynamic data driven adaptive multi-scale simulation (DDDAMS) based planning and control framework is proposed for effective and efficient surveillance and crowd control via UAVs and UGVs. The framework is mainly composed of integrated planner, integrated controller, and decision module for DDDAMS. The integrated planner, which is designed in an agent-based simulation (ABS) environment, devises best control strategies for each function of (1) crowd detection (vision algorithm), (2) crowd tracking (filtering), and (3) UAV/UGV motion planning (graph search algorithm). The integrated controller then controls real UAVs/UGVs for surveillance tasks via (1) sensory data collection and processing, (2) control command generation based on strategies provided by the decision planner for crowd detection, tracking, and motion planning, and (3) control command transmission via radio to the real system. The decision module for DDDAMS enhances computational efficiency of the proposed framework via dynamic switching of fidelity of simulation and information gathering based on the proposed fidelity selection and assignment algorithms. In the experiment, the proposed framework (involving fast-running simulation as well as real-time simulation) is illustrated and demonstrated for a real system represented by hardware-in-the-loop (HIL) real-time simulation integrating real UAVs, simulated UGVs and crowd, and simulated environment (e.g. terrain). Finally, the preliminary results successfully demonstrate the benefit of the proposed dynamic fidelity switching concerning the crowd coverage percentage and computational resource usage (i.e. CPU usage) under cases with two different simulation fidelities.