涡轮增压汽油机气路预测模型的建立与预测控制（英文）

针对涡轮增压汽油机气路系统中节气门与废气旁通阀动力学耦合、机理建模复杂的问题,本文提出基于神经网络模型的气路系统预测控制方法,实现了节气门与废气旁通阀的协调控制.首先,针对涡轮增压汽油机气路系统map与机理混合描述的特性,利用系统的输入输出数据,采用反向传播神经网络(back propagation neural network,BPNN)训练得到一个非线性气路模型;其次,基于泰勒展开式对预测模型进行线性化,并对模型的精度进行了验证,进而利用该模型预测系统的未来动态;然后,在考虑系统存在输入约束的条件下,设计了一个线性模型预测控制器对节气门与废气旁通阀进行协调控制,实现了进气歧管压力和升压的跟踪控制进而满足发动机的扭矩需求;最后,通过离线仿真和基于d SPACE的快速原型实验(rapid control prototyping,RCP)验证了控制系统的有效性和实时性.     

柴油机两级可调增压系统的匹配仿真

柴油机两级涡轮增压技术优化,是为改进内燃机低速扭矩和瞬态响应特性,提升燃油经济性.对某车用重型柴油机进行两级可调涡轮增压系统的匹配研究,采用GT-power软件建立了两级可调增压系统的计算模型,并利用台架试验数据校准了原机模型;进一步以增压压力为目标控制参数,采用软件中的PID模块分段控制废气旁通阀开度,合理分配高低级增压比,并对柴油机外特性稳定工况进行计算.研究结果表明,匹配后的发动机低速扭矩达到目标,高速比油耗降低.改进方法能在实际工程运用中为发动机增压系统的匹配提供选择.     

车用汽油发动机电子控制系统研究现状与展望

发动机电子控制系统是高性能、高可靠性发动机开发的核心研究内容,是保证发动机动力性、经济性和排放性的重要因素之一.针对车用汽油发动机,本文首先分析了典型的车用汽油发动机电子控制系统结构,然后围绕电子节气门控制系统、燃油喷射控制系统、点火控制系统、空燃比控制系统、怠速控制系统、涡轮增压控制系统、爆震检测与控制系统以及汽油机先进燃烧模式控制这8项关键问题展开论述,并着重介绍了近年来国内外的研究内容和研究成果.最后对车用汽油发动机电子控制系统的发展前景和发展方向进行了展望.     

基于稀薄燃烧的CNG发动机涡轮增压控制

为了降低汽车废气对大气的污染,更好地开发与使用污染较少、经济便宜的汽车代用燃料,提出了一种基于稀薄燃烧的压缩天然气(CNG)涡轮增压发动机控制方法;该方法采用中冷涡轮增压,提高了燃气发动机的输出扭矩与功率;又采用基于λ型氧传感器和转矩预估的稀薄燃烧控制策略,燃料消耗量明显降低;用这种控制方法改进中冷增压机型的4D125柴油机,抑制改造后增压发动机高转速时过大扭矩,匹配出CNG燃料发动机;实验证明,这种控制方法实现了涡轮增压燃气发动机的理想外特性,具有较高的可靠性和实用性。     

基于Delphi7.0和FX2N PLC的自动控制液压试验系统研制

自动控制液压试验系统用于对相关试件进行液压爆破试验;针对该试验需求研制了一套试验系统,采用液压系统回路增压和阀控调压的方式,实现系统压力按照设定曲线自动输出;系统实现的关键在于对调压阀的控制和对增压液路的选择;围绕该液压回路,在上位机Delphi 7.0环境下对控制系统和交互软件进行了开发,实现了上位机对调压阀的控制;研究了Delphi 7.0与三菱FX-2N PLC的串口通信方法,通过PLC实现了对液压执行机构控制进行液路切换;试验表明,该液压自动控制系统满足250MPa下压力高精度控制要求;该系统操作简便、控制稳定、压力可调,具有较强的工程应用意义.     

基于干扰估计的非对称运动下飞机刹车系统模型预测控制

针对飞机在非对称运动下的双侧机轮协调控制问题, 提出一种基于滑模干扰估计的模型预测控制方法. 首先, 通过对飞机制动过程横纵方向力矩机理分析并分别考虑左右机轮对刹车性能的影响, 建立全面刻画系统动态的地面滑跑动力学模型. 在此基础上, 设计滑模观测器对侧风干扰进行实时估计, 利用补偿机制实现对侧风扰动的有效抑制. 此外, 提出基于前轮荷载状态门限特征和结合系数阈值范围特征的分析方法, 解决切换跑道环境辨识问题. 设计非线性模型预测算法, 实现飞机纵向防滑刹车和横向跑道纠偏的协调控制. 最后, 在侧风干扰、跑道切换以及不对称着陆等情况下进行仿真实验, 验证了所提出的控制策略能够有效提升刹车系统的防滑效率及纠偏性能.     

重型车辆流量控制式ECHPS系统建模与仿真

关于车辆转向液压系统优化控制问题,针对重型商用车辆传统的具有固定助力特性的HPS系统存在的高速时转向操纵安全性差的问题,提出一种旁通流量控制式电控液压助力转向(ECHPS)系统,建立了ECHPS的机械和液压子系统模型及整车二自由度动力学模型。分析了转向系统可变助力特性的设计要求,建立了ECHPS助力特性MATLAB/Simulink仿真模型,通过仿真计算得到了ECHPS在不同车速下的转向助力特性曲线。仿真结果表明,所设计的ECHPS可变助力特性同时满足了低速时的转向轻便性要求和高速时的良好转向"路感"和操纵稳定性,并得到旁通流量与车速的关系曲线为设计ECHPS旁通流量阀开度的控制策略提供了基本依据。     

非线性四容水箱预测控制研究

根据工业现场实际控制中实现预测控制的困难,提出了利用OPC通讯技术把Matlab与PLC/HMI工业控制系统进行集成,并实现预测控制的方案。文中还针对非线性四容水箱系统建立了机理模型,并通过在线辨识获得四容水箱模型的未知参数,从而对水箱液位进行控制。实际控制结果表明,预测控制能够很好地使水箱液位跟踪设定值,验证了该控制策略的可行性及有效性。     

电子节气门控制器的设计

针对电子节气门系统结构复杂的特点,提出了一种新的电子节气门位置传感器智能控制的设计方法。该控制器由TMS320LF4207中央处理器和TLF6209驱动芯片等组成。具体给出了电子节气门系统在发动机控制系统中的应用框架,通过专家自整定PID控制策略,实现对节气门系统的位置控制。电子节气门开度的阶跃响应试验验证了该方法可以对电子节气门实现精确、快速和稳态误差小的闭环控制。     

小型汽油机调速系统建模与仿真研究

为了提高汽油机调速性能,采用微处理器的电子调速器逐渐取代了机械式调速器。电子调速系统性能好坏直接影响到控制精度。如果能预先建立调速系统计算机仿真模型,则可以为研制性能优良的调速系统提供强有力理论依据,可以少进行试验并节省人力、物力和财力。该文首先根据四冲程通用汽油机工作原理,利用MATLAB工具建立了汽油机计算机仿真模型,给出了仿真结果以及试验对比结果;建立了基于节气门控制的汽油机调速系统计算机仿真模型,并给出了仿真结果。     

Nonlinear internal model controller design for wastegate control of a turbocharged gasoline engine

Internal Model Control (IMC) has a great appeal for automotive powertrain control in reducing the control design and calibration effort. Motivated by its success in several automotive applications, this work investigates the design of nonlinear IMC for wastegate control of a turbocharged gasoline engine. The IMC design for linear time-invariant (LTI) systems is extended to nonlinear systems. To leverage the available tools for LTI IMC design, the quasi-linear parameter-varying (quasi-LPV) models are explored. IMC design through transfer function inverse of the quasi-LPV model is ruled out due to parameter variability. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model in the IMC structure. The controller based on structured quasi-LPV model inverse is designed to achieve boost-pressure tracking. Finally, simulations on a validated high-fidelity model are carried out to show the feasibility of the proposed IMC. Its closed-loop performances are compared with a well-tuned PI controller with extensive feedforward and anti-windup built in. Robustness of the nonlinear IMC design is analyzed using simulations.     

Co-surge in bi-turbo engines: Measurements,analysis and control

In parallel turbocharged V-engines, with two separate air paths connected before the throttle, an oscillation in the flow can occur. If the compressor operates close to the surge line, typically during low speed and high load, and a disturbance alters the mass flow balance, the compressors can begin to alternately go into surge. This phenomenon is called co-surge and is unwanted due to high noise and risk of turbocharger destruction. Co-surge is measured on a test vehicle in a chassis dynamometer and the system analyzed and modeled using a mean value engine model. The investigation shows that alternating compressor speeds have an important role in the prolonged oscillation. A reconstruction of the negative flow from measurements is made and compared to simulation results, showing similar amplitudes, and supports the model validation. A new co-surge detection algorithm is presented, suitable for a pair of sensors measuring either mass flow, boost pressure or turbo speed in the two air paths. Furthermore, a new controller is proposed that uses a model based feedforward for the throttle, together with wastegate actuation to force the compressor speeds together and improve balance at the recovery point. This has been shown to be sufficient for moderate to high pressure ratios over the throttle, and only for zero or very low pressure drop the use of bypass valves is necessary. The advantage of not opening the bypass valves is a smaller drop in boost pressure which also reduces the torque disturbance. The performance of the controller is evaluated both in simulation and in the test vehicle.     

Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.     

Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.     

Diesel engine air path control based on neural approximation of nonlinear MPC

This paper deals with a control design problem for a diesel engine air path system that has strong nonlinearity and requires multi-input and multi-output control to satisfy requirements and constraints. We focus on a neural network based approximation of nonlinear model predictive control (NMPC) for high-speed computation. Most neural approximation methods are verified only through simulation; further, the influence of approximation on the closed-loop performance has been not sufficiently discussed. In this study, we discuss this influence, and propose a new method to improve stability against degradation due to an approximation error. The control system is assembled using a neural network based controller, obtained by the proposed method, and an unscented Kalman filter. This system is verified both numerically and experimentally; the results demonstrate the capability of the proposed method to track the boost pressure, EGR rate, and pumping loss according to the reference values, and satisfy the constraints of compressor surge and choke. The high computation speed that can be achieved using a standard on-board ECU is also demonstrated using the approximated controller.     

Neural network-based sliding mode control of electronic throttle

A neural network-based sliding mode controller for an electronic throttle of an internal combustion engine is proposed. Electronic throttle is modeled as a linear system with uncertainties and affected by disturbances depending on the states of the system. The disturbances, consisting of an unknown friction and a torque caused by the dual spring mechanism inside the mechanical part of the throttle, are estimated by a neural network whose parameters are adapted on-line. The sliding mode controller and the parameters adaptation scheme are derived in order to achieve a tracking of a smooth reference signal, while preserving boundedness of all signals in the closed-loop system. Experimental results are presented which demonstrate the efficiency and robustness of the proposed control scheme.     

Combustion timing control of HCCI engine based on NNPC and Elman-NN model under complex conditions

Homogeneous Charge Compression Ignition (HCCI) combines the characteristics of gasoline engine and diesel engine with high thermal efficiency and low emissions. However, since there is no direct initiator of combustion, it is difficult to control the combustion timing in HCCI engines under complex working conditions. In this paper, Neural Network Predictive Control (NNPC) for combustion timing of the HCCI engine is designed and implemented. First, the black box model based on Elman neural network is designed and developed to estimate the combustion timing. The fuel equivalence ratio, intake valve closing timing, intake manifold temperature, intake manifold gas pressure, and engine speed are chosen as the system inputs. Then, a NNPC controller is designed to control combustion timing by controlling the intake valve closing timing. Simulation results show that the Elman neural network black box model is capable of estimating the HCCI engine combustion timing. In addition, regardless of whether the HCCI engine is in constant or complex condition, the designed NNPC controller is capable of keeping the combustion timing within the ideal range. In particular, under New European Driving Cycle (NEDC) working conditions, the maximum overshoot of the controller is 28.95% and the average error is 1.03 crank angle degree. It is concluded that the controller has good adaptability and robustness.     

Model predictive controller with average emissions constraints for diesel airpath

Diesel airpath controllers are required to deliver good tracking performance whilst satisfying operational constraints and physical limitations of the actuators. Due to explicit constraint handling capabilities, model predictive controllers (MPC) have been successfully deployed in diesel airpath applications. Previous MPC implementations have considered instantaneous constraints on engine-out emissions in order to meet legislated emissions regulations. However, the emissions standards are specified over a drive cycle, and hence, can be satisfied on average rather than just instantaneously, potentially allowing the controller to exploit the trade-off between emissions and fuel economy. In this work, an MPC is formulated to maximise the fuel efficiency whilst tracking boost pressure and exhaust gas recirculation (EGR) rate references, and in the face of uncertainties, adhering to the input, safety constraints and constraints on emissions averaged over some finite time period. The tracking performance and satisfaction of average emissions constraints using the proposed controller are demonstrated through an experimental study considering the new European drive cycle.     

一种航空发动机导叶角度调节器控制方法研究

航空发动机静子导流叶片角度数字电子控制系统的性能和可靠性对发动机的正常工作十分重要;为获得发动机的最优性能,提高飞行可靠性,并保证压气机工作稳定性,文章提出了一种基于RBF (Radial Basis Function)神经网络的PID控制器,构建了3层神经网络数学模型;在AMESim软件平台上,建立了该航空发动机导叶控制系统的数学模型,在Matlab/Simulink中搭建了RBF神经网络控制器;仿真结果表明,在相同参数设置下,本文所设计的控制器与传统PID控制器相比能够实现导叶角度调节器作动筒位移的更加快速、精确控制,表明该控制器设计方法是可行、有效的.     

基于LS-SVM的涡轮增压发动机性能预测

针对神经网络方法在涡轮增压发动机性能预测方面存在的缺陷,提出了一种新的基于最小二乘支持向量机的涡轮增压发动机性能智能预测方法.介绍了最小二乘支持向量机的基本算法,分析了涡轮增压发动机的性能指标,选择发动机转速、压缩比、容积效率、平均指示压力和平均制动压力作为预测模型的输入参数,输出功率、输出扭矩和有效燃油消耗率作为预测模型的输出量,进一步建立了基于最小二乘支持向量机的涡轮增压发动机性能预测模型.仿真实例的预测结果表明,所建立的智能涡轮增压发动机性能预测模型是合理有效的.