WO2021253677A1 - 一种基于负载变化率主动观测的自学习发动机转速控制方法 - Google Patents
一种基于负载变化率主动观测的自学习发动机转速控制方法 Download PDFInfo
- Publication number
- WO2021253677A1 WO2021253677A1 PCT/CN2020/118124 CN2020118124W WO2021253677A1 WO 2021253677 A1 WO2021253677 A1 WO 2021253677A1 CN 2020118124 W CN2020118124 W CN 2020118124W WO 2021253677 A1 WO2021253677 A1 WO 2021253677A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- torque
- load
- engine speed
- change rate
- indicated
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000008859 change Effects 0.000 title claims abstract description 37
- 239000000446 fuel Substances 0.000 claims abstract description 30
- 238000002347 injection Methods 0.000 claims abstract description 29
- 239000007924 injection Substances 0.000 claims abstract description 29
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 8
- 230000009471 action Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 239000000243 solution Substances 0.000 description 8
- 230000003044 adaptive effect Effects 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 244000145845 chattering Species 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D31/00—Use of speed-sensing governors to control combustion engines, not otherwise provided for
- F02D31/001—Electric control of rotation speed
- F02D31/007—Electric control of rotation speed controlling fuel supply
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/14—Introducing closed-loop corrections
- F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
- F02D41/1402—Adaptive control
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
- F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
- F02D41/2425—Particular ways of programming the data
- F02D41/2429—Methods of calibrating or learning
- F02D41/2451—Methods of calibrating or learning characterised by what is learned or calibrated
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/30—Controlling fuel injection
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/30—Controlling fuel injection
- F02D41/32—Controlling fuel injection of the low pressure type
- F02D41/34—Controlling fuel injection of the low pressure type with means for controlling injection timing or duration
- F02D41/345—Controlling injection timing
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D2200/00—Input parameters for engine control
- F02D2200/02—Input parameters for engine control the parameters being related to the engine
- F02D2200/10—Parameters related to the engine output, e.g. engine torque or engine speed
- F02D2200/1002—Output torque
- F02D2200/1004—Estimation of the output torque
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D2200/00—Input parameters for engine control
- F02D2200/02—Input parameters for engine control the parameters being related to the engine
- F02D2200/10—Parameters related to the engine output, e.g. engine torque or engine speed
- F02D2200/101—Engine speed
Definitions
- the present disclosure relates to the technical field of engine speed control, and in particular to a self-learning engine speed control method based on active observation of load change rate.
- Speed control is one of the important functions of engine control.
- the quality of speed control has a significant impact on the fuel consumption and comfort of the engine in idling conditions, the stability of the voltage and power of the engine-driven generator, and the smoothness of the mode transition process in the hybrid power system.
- engine speed control is not a new problem, the problem of unknown load torque has not been well solved, which fundamentally restricts the improvement of speed control quality.
- Proportional-derivative-integral (PID) control is the most commonly used speed control algorithm.
- PID Proportional-derivative-integral
- Robust control is a controller with relatively stable performance, and it has also been tried to be applied to speed control, such as in the literature (Hrovat, Devor, and Jing Sun. "Models and control methodologies for IC engine idle speed control design.” Control Engineering Practice5 .8(1997):1093-1100.).
- the design of the robust controller is conservative, which limits the response speed of its transient process.
- Yin et al. proposed a speed control algorithm based on fuzzy logic, but the design rules of fuzzy logic are more complicated (Yin, Xiaofeng, Dianlun Xue, and Yun Cai. "Application of time-optimal strategy and fuzzy logic to the engine speed control during the gear -shifting process of AMT. "Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). Vol. 4. IEEE, 2007.).
- Shu et al. used nonlinear model predictive control (NMPC) method to carry out speed control, but NMPC has a large amount of calculation and a high demand for model accuracy, and its application in embedded systems is limited to a certain extent (Li, Shu, Hong Chen, and Shuyou Yu.
- a control algorithm that is simple to calibrate, has a small amount of calculation, can directly estimate the load torque or its rate of change, and has an adaptive ability.
- the purpose of the present disclosure is to provide a self-learning speed control method based on active observation of load change rate in response to the problem of poor speed control quality caused by unknown load torque in engine speed control in the prior art.
- a self-learning control method for engine speed based on active observation of load change rate which includes the following steps:
- Step 1 Calculate the moment of inertia through feedback control according to the deviation between the target engine speed and the actual engine speed; use the friction torque model to estimate the current friction torque to obtain the friction torque;
- Step 2 On the basis of the dynamic change of the engine speed, increase the load torque and the load torque change rate of the two "expanded states" to construct a speed dynamic model with the expanded state;
- Step 3 For the dynamic model of the rotational speed with the expansion state, perform online iteration through the observer, and combine the friction torque obtained in step 1 to observe the load torque and load torque change rate online to obtain an estimate of the load torque value;
- Step 4 On the basis of the moment of inertia obtained in step 1, use the estimated value of the load torque obtained in step 3 for compensation to obtain an effective torque; superimpose the friction torque in step 1 on the basis of the effective torque To obtain the indicated torque;
- Step 5 Combining the indicated thermal efficiency and the indicated torque, the fuel injection amount is calculated through the indicated torque model of the engine, and the fuel injection control system controls the rotation speed according to the fuel injection amount.
- the moment of inertia u 0 k p ( ⁇ ref - ⁇ ), ⁇ ref is the target engine speed, ⁇ is the actual engine speed, and k p is the proportional coefficient.
- the dynamic model of the rotational speed with the expanded state is:
- ⁇ is the actual engine speed, Represents the derivative of the actual engine speed
- J is the crankshaft rotational inertia of the system, M i is the indicated torque;
- M Fri is the friction torque described in step 1;
- M load is the load torque;
- Yes The derivative of Is the rate of change of equivalent load torque, and h is the derivative of the rate of change of equivalent load torque.
- step 3 the observer in step 3 is:
- ⁇ and ⁇ are intermediate variables
- ⁇ 1 and ⁇ 2 are the observer gains
- ⁇ is the actual engine speed
- ⁇ o is the observer bandwidth
- u 0 is the moment of inertia obtained in step 1
- the indicated torque is
- the indicated torque model is:
- M i is the indicated torque obtained in Step 4
- ⁇ i is the indicated thermal efficiency
- n cyl is the number of engine cylinders
- ⁇ is the engine actual rotation speed.
- the indicated thermal efficiency in step 5 is a fixed value between 0-1 (not including 0 and 1) assigned artificially, or the indicated thermal efficiency is obtained through online learning of model parameters.
- the current judgment is a steady-state operating condition, that is, when the relative fluctuation range of the load torque is within 3%, a 1%-10% sinusoidal disturbance is added to the original fuel injection signal amplitude and injected into the engine; , The engine speed will fluctuate slightly under the action of the sinusoidal disturbance signal; according to the current engine fuel injection, the actual engine speed and friction torque, the online estimation algorithm is used to calculate the indicated thermal efficiency online.
- the recursive least square method is used to perform online learning on the indicated thermal efficiency to obtain the estimated value of the indicated thermal efficiency ⁇ i
- the calculation process is as follows:
- the controller can actively adapt to changes in the engine's operating characteristics due to aging and malfunctions, and avoid a decrease in control performance.
- Fig. 1 is a control block diagram of the present disclosure.
- Fig. 2 is a block diagram of the indicated thermal efficiency learning algorithm in the present disclosure.
- Figures 3a to 3d are comparison diagrams of the active disturbance rejection controller of the present disclosure and the traditional PID controller.
- a self-learning control method for engine speed based on active observation of load change rate including the following steps:
- Step 1 Calculate the required moment of inertia through feedback control according to the deviation between the target engine speed and the actual engine speed; use the friction torque model to estimate the current friction torque to obtain the friction torque;
- Step 2 For the dynamic change process of the engine speed, on the basis of the speed dynamics, increase the load torque and load torque change rate in two "expanded states";
- Step 3 Using a reduced-order expanded state observer method, combined with the friction torque, online observation of load torque and load torque change rate, to obtain an estimated value of load torque;
- Step 4 On the basis of the moment of inertia obtained in step 1, use the estimated value of the load torque observed in step 3 for compensation to obtain the required effective torque; superimpose the friction torque on the basis of the effective torque, Obtain the required indicated torque;
- Step 5 Combining the indicated torque and indicated thermal efficiency, the fuel injection amount is calculated through the indicated torque model of the engine, and the fuel injection control system controls the speed according to the fuel injection amount.
- step 5 can be processed in the following two ways:
- the indicated thermal efficiency adopts a fixed value between 0-1 artificially assigned (excluding 1 and 0).
- Method two in order to adapt to the changes in thermal efficiency of the engine due to aging and wear, the online learning of model parameters is used to optimize the indicated thermal efficiency in step 5, and the indicated thermal efficiency is between 0-1 obtained by online learning of model parameters. Numerical value (excluding 1 and 0).
- step 4 online estimation of the indicated thermal efficiency is performed to obtain the indicated thermal efficiency. In this way, the accuracy of step 4 is continuously improved, and the adaptation of engine characteristic changes is realized. Then, the indicated thermal efficiency is converted into the required fuel injection quantity, which is handed over to the fuel injection control system to complete the speed control.
- the calculation method of the moment of inertia u 0 is:
- ⁇ ref is the target engine speed (unit: rpm)
- ⁇ is the actual engine speed (unit: rpm)
- the engine can be a diesel engine
- k p is a proportional coefficient, which can be adjusted according to the required speed response speed.
- the differential equation model of the engine speed adds two "expanded states" to obtain the speed dynamic model with the expanded state:
- ⁇ is the actual engine speed (unit: rpm)
- J is the crankshaft rotation system moment of inertia (unit: kg ⁇ m 2)
- M i is the indicated torque (unit: Nm)
- M Fri is the friction torque (unit: Nm)
- M load is the load Torque (unit: Nm).
- the reduced-order expanded state observer method is adopted to design an observer for the rotational speed dynamic model with the expanded state, and perform online iteration to obtain the estimated value of the load torque, and the observer is:
- ⁇ and ⁇ are intermediate variables
- ⁇ 1 and ⁇ 2 are the observer gains
- ⁇ is the actual engine speed (unit: rpm)
- ⁇ o is the observer bandwidth (unit: rad/s)
- Equivalent load torque The estimated value of, and go one step further, using the equivalent load torque The estimated value of is converted to an estimated value of load torque.
- the equivalent effective torque Describes the fuel injection volume (Unit: kg/s)
- the gain relationship between the indicated torque that is, the indicated torque ⁇ i is indicative of thermal efficiency (a value from 0 to 1)
- n cyl is the number of engine cylinders
- H LHV is the low heating value of the diesel engine (unit: J/(kg*K)), which is a constant.
- the online estimation method of friction torque and the friction torque model described in step 4 can refer to the literature (Xie Hui, Liu Xiao. "Online learning algorithm of friction torque for idling and shutdown data.” Journal of Tianjin University (Natural Science) And Engineering Technology Edition) 7(2016): 14.), so I won’t repeat it here.
- the method for optimizing the parameters of the indicated torque model in step 5 is: if the current judgment is a steady-state operating condition, that is, when the relative fluctuation range of the load torque is within 3%, (judgment standard is The variance of the fluctuation of the fuel injection volume within n seconds is less than x%, where n and x are parameters manually set according to the engine and operating conditions, the recommended value of n is 3; the recommended value of x is 10.0), then the original injection Based on the amplitude of the fuel quantity signal, a sinusoidal disturbance of about 5% (range: 1% to 10%) is injected into the engine; after that, the engine speed will fluctuate slightly under the action of the sinusoidal disturbance signal; according to the current The fuel injection quantity, speed of the engine, and the friction torque estimated according to the engine speed and oil temperature are used to calculate the indicated thermal efficiency online using the online estimation algorithm to obtain the indicated thermal efficiency ⁇ i in formula (3). If it is currently judged to be an unsteady operating condition, the learning algorithm
- the online iterative algorithm can be used to estimate the parameters online.
- the specific method can be recursive least squares, but it is not limited to this method:
- step 5 In order to indicate the estimated value of the thermal efficiency ⁇ i , use the estimated value as the indicated thermal efficiency in step 5, convert it into the required fuel injection volume, and hand it over to the fuel injection control system to control the speed.
- Figs. 3a to 3d Comparing the control algorithm of the present disclosure (denoted as the active disturbance rejection controller) with the traditional PID controller, the results are shown in Figs. 3a to 3d.
- the accused object was a six-cylinder 12L heavy-duty diesel engine.
- Figure 3a is a comparison of speeds
- Figure 3b is an enlarged view of the dashed circle region in Figure 3a
- Figure 3c is a comparison of load torque
- Figure 3d is an enlarged view of the dashed circle region in Figure 3c.
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
- Electrical Control Of Air Or Fuel Supplied To Internal-Combustion Engine (AREA)
Abstract
一种基于负载变化率主动观测的自学习转速控制方法，包括以下步骤：计算得到转动惯性力矩和摩擦扭矩；步骤2，在发动机的转速动态变化的基础上，增加负载扭矩和负载扭矩变化率两个"扩张状态"；步骤3，通过观测器对转速、负载扭矩及负载扭矩变化率进行在线观测；步骤4，利用负载扭矩的估计值做补偿，得到有效扭矩；在有效扭矩的基础上叠加摩擦扭矩，获得指示扭矩；步骤5，通过发动机的指示扭矩计算得到喷油量，喷油控制系统根据喷油量控制转速。该方法通过补偿负载扭矩，提高了转速控制的抗干扰能力。
Description
本公开涉及发动机转速控制技术领域，特别是涉及一种基于负载变化率主动观测的自学习发动机转速控制方法。
转速控制是发动机控制的重要功能之一。转速的控制品质对于发动机怠速工况的油耗和舒适性、发动机驱动的发电机的电压和功率的稳定性，以及混合动力系统中模式过渡过程的平顺性影响显著。虽然发动机转速控制不是一个新问题，但负载扭矩未知这一难题一直没有很好的解决，从根本上制约了转速控制品质的提升。
比例-微分-积分(PID)控制是最常用的转速控制算法。但是，为了保证控制品质，通常需要复杂的PID参数标定。鲁棒控制是一种性能比较稳定的控制器，也被尝试着应用于转速控制，如文献(Hrovat，Devor，and Jing Sun.″Models and control methodologies for IC engine idle speed control design.″Control Engineering Practice5.8(1997)：1093-1100.)所示。但是，鲁棒控制器的设计比较保守，限制了其瞬态过程的响应速度。Song等人提出了基于线性变参数(LPV)模型的转速控制器，然而这种LPV模型的设计过程却比较复杂(Song，Qingwen，and Karolos M.Grigoriadis.″Diesel engine speed regulation using linear parameter varying control.″Proceedings of the 2003 American Control Conference，2003..Vol.1.IEEE，2003.)。Sun等人提出了转速的最优控制算法，然而最优控制在鲁棒性上存在局限性，限制了其工程应用(Sun，Pu，B.Powell，and Davor Hrovat.″Optimal idle speed control of an automotive engine.″Proceedings of the 2000 American Control Conference.ACC(IEEE Cat.No.00CH36334).Vol.2.IEEE，2000.)。Yin等人提出基于模糊逻辑的转速控制算法，但是模糊逻辑的设计规则比较复杂(Yin，Xiaofeng，Dianlun Xue，and Yun Cai.″Application of time-optimal strategy and fuzzy logic to the engine speed control during the gear-shifting process of AMT.″Fourth International Conference on Fuzzy Systems and Knowledge Discovery(FSKD 2007).Vol.4.IEEE，2007.)。Shu等人采用非线性模型预测控制(NMPC)的方法开展了转速控制，但是NMPC的计算量大，对模型精度的需求高，在嵌入式系统中应用存在一定程度的受限(Li，Shu，Hong Chen，and Shuyou Yu.″Nonlinear model predictive control for idle speed control of SI engine.″Proceedings of the 48h IEEE Conference on Decision and Control(CDC)held jointly with 2009 28th Chinese Control Conference.IEEE，2009.)。Feng等人提出了基于自适应算法转速控制方法(Feng，Meiyu，and Xiaohong Jiao.″Double closed-loop control with adaptive strategy for automotive engine speed tracking system.″International Journal of Adaptive Control and Signal Processing31.11(2017)：1623-1635.)，但是，该算法并没有直接解决负载扭矩的不确定性问题。Stotsky等人针对未知的干扰，提出了变结构的怠速控制算法，如参考文献(Stotsky，Alexander，Bo Egardt，and
Eriksson.″Variable structure control of engine idle speed with estimation of unmeasurable disturbances.″J.Dyn.Sys.，Meas.，Control 122.4(2000)：599-603.)所示。然而，滑模控制中的震颤问题一直没有能够很好地解决。
除此之外，随着发动机使用时间的增长，发动机的热效率等特性会因喷油系统老化、轴系摩擦阻力的上升等发生老化。传统控制算法不能有效感知这种特性变化，容易导致转速控制的品质下降。
综合上述分析，为了提升发动机转速的控制品质，需要一种标定简单、计算量小、能够直接对负载扭矩或其变化率进行估计，并且具有自适应能力的控制算法。
发明内容
本公开的目的是针对现有技术中存在的发动机转速控制中由于负载扭矩未知而造成的转速控制品质差的问题，而提供一种基于负载变化率主动观测的自学习转速控制方法。
为实现本公开的目的所采用的技术方案是：
一种基于负载变化率主动观测的发动机转速自学习控制方法，其中，包括以下步骤：
步骤1，根据发动机目标转速和发动机实际转速的偏差，通过反馈控制计算转动惯性力矩；利用摩擦扭矩模型估计当前的摩擦扭矩，得到摩擦扭矩；
步骤2，在发动机的转速动态变化的基础上，增加负载扭矩和负载扭矩变化率两个“扩张状态”，构建带有扩张状态的转速动态模型；
步骤3，针对于所述带有扩张状态的转速动态模型，通过观测器进行在线迭代，结合步骤1得到的所述摩擦扭矩，对负载扭矩及负载扭矩变化率进行在线观测，得到负载扭矩的估计值；
步骤4，在步骤1得到的所述转动惯性力矩的基础上，利用步骤3得到的负载扭矩的估计值做补偿，得到有效扭矩；在所述有效扭矩的基础上叠加步骤1所述的摩擦扭矩，获得指示扭矩；
步骤5，结合指示热效率和所述指示扭矩，通过发动机的指示扭矩模型计算得到喷油量，喷油控制系统根据所述喷油量控制转速。
在上述技术方案中，所述步骤1中，转动惯性力矩u
0＝k
p(ω
ref-ω)，ω
ref为发动机目 标转速，ω是发动机实际转速，k
p为比例系数。
在上述技术方案中，所述步骤2中，带有扩张状态的转速动态模型为：
其中，ω是发动机实际转速，
代表发动机实际转速的导数；
在上述技术方案中，所述步骤3中的观测器为：
其中，ε和ξ为中间变量，β
1和β
2为观测器增益，ω为发动机实际转速，ω
o为观测器带宽，
为等效负载扭矩
的估计值，利用等效负载扭矩
的估计值除以
得到负载扭矩M
load的估计值。
在上述技术方案中，所述步骤4中，
在上述技术方案中，所述步骤5中，指示扭矩模型为：
其中，
为喷油量，M
i是步骤4所得的指示扭矩，H
LHV为柴油机的低热值，η
i是指示热效率，n
cyl是发动机气缸个数，ω是发动机实际转速。
在上述技术方案中，所述步骤5中的指示热效率为人为赋予的0-1之间的定值(不包括0和1)或者指示热效率为通过模型参数在线学习的方式获得0-1之间的数值(不包括0和1)。
在上述技术方案中，当指示热效率为通过模型参数在线学习的方式获得时，步骤如下：
如果当前判断为稳态工况，即负载扭矩的相对波动幅度在3％以内时，则在原有的喷油量信号幅值的基础上增加1％-10％的正弦扰动，喷入发动机；之后，发动机转速会在正弦扰动信号的作用下发生轻微波动；根据当前的发动机喷油量、发动机实际转速和摩擦扭矩，利用在线估计算法，对指示热效率进行在线计算。
在上述技术方案中，采用递推最小二乘法，对指示热效率进行在线学习，得到指示热效率η
i的估计值
的计算过程如下：
由
和
得到
定义
对于多个采样点，Y＝[y(1) y(2) y(3) ... y(n)]
T，
有：
Y＝φη
i，进行在线迭代，得到：
与现有技术相比，本公开的有益效果是：
1.通过对负载扭矩的主动观测，从根本上解决了造成发动机转速波动的原因，显著提高了转速控制的抗干扰能力。
2.在观测器中对负载的变化率进行了主动观测，比传统的观测负载的方法速度更快，进一步提升了转速的控制品质。
3.通过设计指示热效率的在线学习算法，使得控制器能够对发动机因老化和故障造成的运行特性的变化进行主动适应，避免控制性能的下降。
4.通过使用扩张状态观测器，显著提升了控制器的鲁棒性，全工况只需要一套控制参数。与比传统PID控制器相比，标定工作量降低了80％以上。
5.本算法计算简单，内存占用不到2kBytes，在200MHz的单片机上运行时间约10微秒。比传统的MPC等基于模型的控制算法更适用于嵌入式系统。
图1是本公开的控制框图。
图2是本公开中的指示热效率学习算法框图。
图3a～图3d是本公开主动抗扰控制器与传统的PID控制器的对比图。
以下结合具体实施例对本公开作进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本公开，并不用于限定本公开。
实施例1
一种基于负载变化率主动观测的发动机转速自学习控制方法，包括以下步骤：
步骤1，根据发动机目标转速和发动机实际转速的偏差，通过反馈控制计算所需要的转动惯性力矩；利用摩擦扭矩模型估计当前的摩擦扭矩，得到摩擦扭矩；
步骤2，针对发动机的转速动态变化过程，在转速动态的基础上，增加负载扭矩和负载扭矩变化率两个“扩张状态”；
步骤3，采用降阶扩张状态观测器方法，结合所述摩擦扭矩，对负载扭矩及负载扭矩变化率进行在线观测，得到负载扭矩的估计值；
步骤4，在步骤1得到的所述转动惯性力矩的基础上，利用步骤3观测到的负载扭矩的估计值做补偿，得到所需的有效扭矩；在所述有效扭矩的基础上叠加摩擦扭矩，获得所需的指示扭矩；
步骤5，结合所述指示扭矩和指示热效率，通过发动机的指示扭矩模型计算得到喷油量，喷油控制系统根据所述喷油量控制转速。
更进一步的，所述步骤5的指示热效率可采用以下两种方式处理：
方式一，指示热效率采用人为赋予的0-1之间的定值(不包括1和0)。
方式二，为了适应发动机因为老化磨损造成的热效率的变化，采用模型参数在线学习的方式，对所述步骤5中的指示热效率进行优化，指示热效率采用模型参数在线学习获得的0-1之间的数值(不包括1和0)。
更进一步的，利用喷油量和转速的动态关系以及步骤1得到的摩擦扭矩和步骤4中得到的指示扭矩，对指示热效率进行在线估计，得到所述指示热效率。从而不断提高步骤4的准确性，实现发动机特性变化的自适应。再由所述指示热效率换算为所需的喷油量，交给喷油控制系统，完成转速控制。
实施例2
进一步的，所述步骤1中，所述转动惯性力矩u
0的计算方法为：
u
0＝k
p(ω
ref-ω) (1)
其中：ω
ref为发动机目标转速(单位：rpm)，ω是发动机实际转速(单位：rpm)，发动机可为柴油机，k
p为比例系数，可以根据所需要的转速响应速度调节。
更进一步的，所述步骤2中，发动机转速的微分方程模型增加两个“扩张状态”，得到带有扩张状态的转速动态模型：
其中，ω是发动机实际转速(单位：rpm)，
代表发动机实际转速的导数，J是曲轴转动系统的转动惯量(单位：kg·m
2)，M
i是指示扭矩(单位：Nm)，M
Fri是摩擦扭矩(单位：Nm)，M
load是负载扭矩(单位：Nm)。
为简化(2)的表达，令
作为等效负载扭矩，是负载扭矩的
倍，
作为等效摩擦扭矩，是摩擦扭矩的
倍，将(2)改写为：
将
和
的变化率都视为扩张的状态，将公式(3)改写为
其中，
是
的导数，
是等效负载扭矩的变化率，h是等效负载扭矩的变化率的导数，是未知的。
进一步的，所述步骤3中，采用降阶扩张状态观测器方法，针对所述带有扩张状态的转速动态模型设计观测器，进行在线迭代，得到负载扭矩的估计值，所述观测器为：
其中，ε和ξ为中间变量，β
1和β
2为观测器增益，ω为发动机实际转速(单位rpm)，ω
o为观测器带宽(单位：rad/s)，
为等效负载扭矩
的估计值，再进一步，利用等效负载扭矩
的估计值转换得到负载扭矩的估计值。
进一步的，所述步骤5中，指示扭矩模型的完整数学表达为公式(6)：
其中，
为有效扭矩的
倍，称为等效有效扭矩，
描述的是喷油量
(单位：kg/s)与指示扭矩之间的增益关系，即指示扭矩
η
i是指示热效率(0到1的数值)，n
cyl是发动机气缸个数，
是喷油量(单位：kg/s)，H
LHV为柴油机的低热值(单位：J/(kg*K))，为常数。
进一步的，摩擦扭矩的在线估计方法以及步骤4中所述的摩擦扭矩模型可以参考文献(谢辉，刘晓.″怠速和停机数据拟合的摩擦扭矩在线学习算法.″天津大学学报(自然科学与工程技术版)7(2016)：14.)，在此不再赘述。
采用模型参数在线学习的方式，对步骤5中的指示扭矩模型的参数进行优化的方法为：如果当前判断为稳态工况，即负载扭矩的相对波动幅度在3％以内时，(判断标准为连续n秒内的喷油量波动的方差小于x％，其中n和x是根据发动机和使用条件人工设定的参数，n的推荐值为3；x推荐值为10.0)，则在原有的喷油量信号幅值的基础上增加约5％左右(范围：1％到10％)的正弦扰动，喷入发动机；之后，发动机转速会在正弦扰动信号的作用下会发生轻微的波动；根据当前的发动机喷油量、转速，以及根据发动机转速和机油温度估算的摩擦扭矩，利用在线估计算法，对指示热效率进行在线计算，获得公式(3)中的指示热效率η
i。如果当前判断为非稳态工况，则关闭指示扭矩模型参数的学习算法。
上述过程的具体实现方法为，将
和
及
代入公式(2)，得到：
将微分方程变为差分方程，
经公式变形后可得：
定义
对于多个采样点，即
Y＝[y(1) y(2) y(3) ... y(n)]
T，
有：
Y＝φη
i (9)
对于(9)采用在线迭代算法，可以对参数进行在线估计。具体方法可以用递推最小二乘，但不限于该方法：
其中，
为指示热效率η
i的估计值，利用该估计值作为步骤5中的指示热效率，换算为所需的喷油量，交给喷油控制系统控制转速。
对比例1
将本公开的控制算法(记为主动抗扰控制器)与传统的PID控制器进行对比，结果如附图3a～图3d所示。被控对象是一台六缸12L的重型柴油机。其中图3a为转速的对比，图3b为图3a中的虚线圈区域放大图，图3c为负载扭矩的对比，图3d为图3c中的虚线圈区域放大图。
由结果可知，本算法在遇到负载扭矩干扰(譬如第11s的负载突增情景)后，转速的跌落幅度比传统的PID算法小50％左右。其根本原因在于，本算法(在图中标注为：主动抗扰控制器)能够对负载扭矩进行准确估计，比PID算法中的ID(积分和微分)控制所求得的等效负载扭矩更快更准确。这一结果也证明了本公开的有效性。
以上所述仅是本公开的优选实施方式，应当指出的是，对于本技术领域的普通技术人员来说，在不脱离本公开原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本公开的保护范围。
Claims (10)
- 一种基于负载变化率主动观测的发动机转速自学习控制方法，其中，包括以下步骤：步骤1，根据发动机目标转速和发动机实际转速的偏差，通过反馈控制计算转动惯性力矩；利用摩擦扭矩模型估计当前的摩擦扭矩，得到摩擦扭矩；步骤2，在发动机的转速动态变化的基础上，增加负载扭矩和负载扭矩变化率两个“扩张状态”，构建带有扩张状态的转速动态模型；步骤3，针对于所述带有扩张状态的转速动态模型，通过观测器进行在线迭代，结合步骤1得到的所述摩擦扭矩，对负载扭矩及负载扭矩变化率进行在线观测，得到负载扭矩的估计值；步骤4，在步骤1得到的所述转动惯性力矩的基础上，利用步骤3得到的负载扭矩的估计值做补偿，得到有效扭矩；在所述有效扭矩的基础上叠加步骤1所述的摩擦扭矩，获得指示扭矩；步骤5，结合指示热效率和所述指示扭矩，通过发动机的指示扭矩模型计算得到喷油量，喷油控制系统根据所述喷油量控制转速。
- 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤1中，转动惯性力矩u 0＝k p(ω ref-ω)，ω ref为发动机目标转速，ω是发动机实际转速，k p为比例系数。
- 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤2中，带有扩张状态的转速动态模型为：
其中，ω是发动机实际转速，代表发动机实际转速的导数；
J是曲轴转动系统的转动惯量，M i是指示扭矩；
作为等效摩擦扭矩，
M Fri是步骤1所述的摩擦扭矩；
作为等效负载扭矩，
M load是负载扭矩；
是
的导数，
是等效负载扭矩的变化率，h是等效负载扭矩的变化率的导数。
- 如权利要求3所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤3中的观测器为：
其中，ε和ξ为中间变量，β 1和β 2为观测器增益，ω为发动机实际转速，ω o为观测器带宽，为等效负载扭矩
的估计值，利用等效负载扭矩
的估计值除以
得到负载扭矩M load的估计值。 - 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤4中，u 0为步骤1所得的转动惯性力矩，
为步骤3得到的负载扭矩的估计值的
倍，
为步骤1所得的摩擦扭矩的
倍，指示扭矩为
- 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤5中，指示扭矩模型为：
其中，为喷油量，M i是步骤4所得的指示扭矩，H LHV为柴油机的低热值，η i是指示热效率，n cyl是发动机气缸个数，ω是发动机实际转速。
- 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤5中的指示热效率为人为赋予的0-1之间的定值。
- 如权利要求1所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，所述步骤5中的指示热效率通过模型参数在线学习的方式获得0-1之间的数值。
- 如权利要求8所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，如果当前判断为稳态工况，则在原有的喷油量信号幅值的基础上增加1％-10％的正弦扰动，喷入发动机；之后，发动机转速会在正弦扰动的作用下发生轻微波动；根据当前的发动机喷油量、发动机实际转速和摩擦扭矩，利用在线估计算法，对指示热效率进行在线计算。
- 如权利要求8所述的基于负载变化率主动观测的发动机转速自学习控制方法，其中，采用递推最小二乘法，对指示热效率进行在线学习，得到指示热效率η i的估计值
的计算过程如下：
的计算过程如下：
由和
得到
定义对于多个采样点，Y＝[y(1) y(2) y(3) ... y(n)] T，
有：
Y＝φη i，进行在线迭代，得到：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE112020007324.6T DE112020007324T5 (de) | 2020-06-15 | 2020-09-27 | Selbstlernendes verfahren zum steuern der motordrehzahl basierend auf der aktiven beobachtung der laständerungsrate |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010543898.2A CN111749800B (zh) | 2020-06-15 | 2020-06-15 | 基于负载变化率主动观测的自学习转速控制方法 |
| CN202010543898.2 | 2020-06-15 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2021253677A1 true WO2021253677A1 (zh) | 2021-12-23 |
Family
ID=72675211
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2020/118124 WO2021253677A1 (zh) | 2020-06-15 | 2020-09-27 | 一种基于负载变化率主动观测的自学习发动机转速控制方法 |
Country Status (3)
| Country | Link |
|---|---|
| CN (1) | CN111749800B (zh) |
| DE (1) | DE112020007324T5 (zh) |
| WO (1) | WO2021253677A1 (zh) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115629620A (zh) * | 2022-12-21 | 2023-01-20 | 北京航空航天大学 | 一种针对侦察任务的无人机主动抗干扰飞行控制方法 |
| CN115977822A (zh) * | 2023-02-02 | 2023-04-18 | 潍柴动力股份有限公司 | 一种喷油参数控制方法、装置、柴油车及存储介质 |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112267950B (zh) * | 2020-10-20 | 2023-05-02 | 东风越野车有限公司 | 混合动力柴油机高精度扭矩控制方法 |
| TWI776535B (zh) * | 2021-06-02 | 2022-09-01 | 台達電子工業股份有限公司 | 基於負載轉矩-轉動慣量自學習模型之速度控制方法與電機驅動系統 |
| CN114706299B (zh) * | 2021-12-21 | 2024-06-11 | 天津大学 | 基于轴齿啮合应力多信息融合观测的发动机-isg扭矩动态协调控制算法 |
| CN114352420B (zh) * | 2022-01-24 | 2023-03-21 | 一汽解放汽车有限公司 | 一种非均匀做功发动机的扭矩控制方法及扭矩控制系统 |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008012607A1 (de) * | 2008-03-05 | 2009-09-10 | Continental Automotive Gmbh | Verfahren und Vorrichtung zur Ermittlung eines Adaptionswertes für die Einstellung eines Luft-Kraftstoff-Verhältnis eines Einspritzsystems eines Verbrennungsmotors |
| CN103291473A (zh) * | 2013-06-09 | 2013-09-11 | 华为技术有限公司 | 发动机组的控制方法及装置 |
| US20190242319A1 (en) * | 2018-02-06 | 2019-08-08 | Hitachi, Ltd. | Control device, control method, and control program |
| CN110805498A (zh) * | 2019-11-11 | 2020-02-18 | 奇瑞汽车股份有限公司 | 一种发动机燃油修正控制方法 |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4277677B2 (ja) * | 2003-06-27 | 2009-06-10 | 株式会社デンソー | ディーゼル機関の噴射量制御装置 |
| JP5751061B2 (ja) * | 2011-07-22 | 2015-07-22 | 株式会社豊田自動織機 | ガソリンエンジンの制御方法およびディーゼルエンジンの制御方法 |
| DE102012111100B4 (de) * | 2012-11-19 | 2024-07-11 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Verfahren zur adaptiven Regelung einer Momentenreserve |
| JP6024700B2 (ja) * | 2014-04-11 | 2016-11-16 | トヨタ自動車株式会社 | エンジン回転速度制御装置 |
| CN106647288B (zh) * | 2017-02-23 | 2019-05-31 | 重庆邮电大学 | 基于非奇异终端滑模观测器的发动机指示扭矩估计方法 |
| CN107989708B (zh) * | 2017-12-08 | 2020-05-29 | 天津大学 | 基于扰动观测的自学习发动机扭矩控制系统及其方法 |
-
2020
- 2020-06-15 CN CN202010543898.2A patent/CN111749800B/zh active Active
- 2020-09-27 WO PCT/CN2020/118124 patent/WO2021253677A1/zh active Application Filing
- 2020-09-27 DE DE112020007324.6T patent/DE112020007324T5/de active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008012607A1 (de) * | 2008-03-05 | 2009-09-10 | Continental Automotive Gmbh | Verfahren und Vorrichtung zur Ermittlung eines Adaptionswertes für die Einstellung eines Luft-Kraftstoff-Verhältnis eines Einspritzsystems eines Verbrennungsmotors |
| CN103291473A (zh) * | 2013-06-09 | 2013-09-11 | 华为技术有限公司 | 发动机组的控制方法及装置 |
| US20190242319A1 (en) * | 2018-02-06 | 2019-08-08 | Hitachi, Ltd. | Control device, control method, and control program |
| CN110805498A (zh) * | 2019-11-11 | 2020-02-18 | 奇瑞汽车股份有限公司 | 一种发动机燃油修正控制方法 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115629620A (zh) * | 2022-12-21 | 2023-01-20 | 北京航空航天大学 | 一种针对侦察任务的无人机主动抗干扰飞行控制方法 |
| CN115977822A (zh) * | 2023-02-02 | 2023-04-18 | 潍柴动力股份有限公司 | 一种喷油参数控制方法、装置、柴油车及存储介质 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111749800B (zh) | 2021-09-10 |
| DE112020007324T5 (de) | 2023-04-06 |
| CN111749800A (zh) | 2020-10-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2021253677A1 (zh) | 一种基于负载变化率主动观测的自学习发动机转速控制方法 | |
| CN111520227B (zh) | 一种发动机电子水泵的控制方法 | |
| CN110182069B (zh) | 一种电动汽车增程器发电功率闭环控制方法 | |
| CN111413872B (zh) | 基于扩张状态观测器的空气容腔压力快速主动抗扰方法 | |
| CN103732895B (zh) | 滑模控制器和内燃机系统控制装置 | |
| CN104514637B (zh) | 动力传动系控制系统 | |
| CN113006950B (zh) | 一种发动机排气蝶阀的控制方法和系统 | |
| CN114123238A (zh) | 一种电解铝负荷参与电力系统调频的卡尔曼滤波控制方法 | |
| CN114263564B (zh) | 考虑不变桨风速范围的风电机组有功功率控制方法及系统 | |
| CN116292848A (zh) | 一种执行和润滑二合一的电子油泵控制方法 | |
| JP2004108368A (ja) | 内燃機関の過給圧調整方法 | |
| CN111930012A (zh) | 一种磁流变执行器的闭环控制方法 | |
| Kumar et al. | Error performance index based PID tuning methods for temperature control of heat exchanger system | |
| CN111188732A (zh) | 一种风力发电变桨鲁棒容错控制方法 | |
| CN113586309A (zh) | 改善发动机动力不足控制方法及系统 | |
| CN117439465A (zh) | 一种基于非匹配干扰补偿的永磁同步电机滑模控制方法 | |
| CN110737197A (zh) | 一种基于无模型的柴油机转速自适应自抗扰控制方法 | |
| CN107620650B (zh) | 一种二冲程点燃式发动机缸间功率不平衡控制方法 | |
| CN113460025B (zh) | 混合动力汽车发动机转速模式下失火诊断方法及电子设备 | |
| Zhang et al. | Model-based design of a variable nozzle turbocharger controller | |
| CN117605589B (zh) | 一种柴油发电机的节能控制方法 | |
| Mu et al. | Study with fuzzy self-turning PID controller on idle speed controlling of automobile | |
| CN114872686B (zh) | 一种发动机冷却水温控制方法 | |
| CN116378841B (zh) | 一种结合神经网络的stc发动机扭矩自适应控制方法 | |
| Ruan et al. | MAP Learning and Disturbance Observation based Engine Torque Control for dynamometer test bench |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20940679 Country of ref document: EP Kind code of ref document: A1 |
|
| 32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 17.05.2023) |
|
| 122 | Ep: pct application non-entry in european phase |
Ref document number: 20940679 Country of ref document: EP Kind code of ref document: A1 |