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CN112433476B - Robust prediction control device and robust prediction control method for networked control system of electric vehicle - Google Patents

Robust prediction control device and robust prediction control method for networked control system of electric vehicle Download PDF

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CN112433476B
CN112433476B CN202110106904.2A CN202110106904A CN112433476B CN 112433476 B CN112433476 B CN 112433476B CN 202110106904 A CN202110106904 A CN 202110106904A CN 112433476 B CN112433476 B CN 112433476B
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曹万科
王乐成
李建威
何洪文
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Abstract

本发明属于电动汽车网络化控制技术及系统领域，具体为电动汽车网络化控制系统鲁棒预测控制装置及其控制方法。该装置包括输入模块、决策模块、输出模块，三者协同工作，可实现电动汽车网络化系统的高性能预测控制。本发明作为一种电动汽车网络化控制系统控制装置，可充分补偿网络时滞对系统控制性能的不良影响，提高以低延时为主要特征的车辆预测控制装置实时性能，增强电动汽车网络化控制系统鲁棒性，确保车辆控制稳定性，为设计高性能车辆控制器以及提升车辆运行安全提供技术方法支持。

Figure 202110106904

The invention belongs to the field of electric vehicle networked control technology and system, in particular to a robust predictive control device and a control method of an electric vehicle networked control system. The device includes an input module, a decision-making module, and an output module, which work together to realize high-performance predictive control of an electric vehicle networked system. As an electric vehicle networked control system control device, the present invention can fully compensate the adverse effects of network time delay on the system control performance, improve the real-time performance of the vehicle predictive control device with low delay as the main feature, and enhance the electric vehicle networked control System robustness ensures vehicle control stability, and provides technical method support for designing high-performance vehicle controllers and improving vehicle operation safety.

Figure 202110106904

Description

电动汽车网络化控制系统鲁棒预测控制装置及其控制方法Robust predictive control device and control method for electric vehicle networked control system

技术领域technical field

本发明属于电动汽车网络化控制技术及系统领域，具体为电动汽车网络化控制系统鲁棒预测控制装置及其控制方法。The invention belongs to the field of electric vehicle networked control technology and system, in particular to a robust prediction control device and a control method of an electric vehicle networked control system.

背景技术Background technique

近年来，得益于车载网络技术的快速发展，电动汽车网络化控制系统克服了传统机械液压结构体积大，响应慢，不易布置等缺陷，提升了控制系统的结构集成程度与控制响应速度。但车载网络的使用将不可避免地引入信号传输延时，将直接影响车辆控制的系统稳定性，进而影响汽车运行安全，成为电动汽车网络化控制技术发展的新挑战。现有的网络化控制方法，如鲁棒

Figure 100002_DEST_PATH_IMAGE002
无穷控制方法，鲁棒增益调度控制方法，不具备显式处理控制问题中约束条件的能力，无法充分补偿网络延时对控制性能的不良影响，致使控制器稳定性降低，均具有一定的局限性。In recent years, thanks to the rapid development of in-vehicle network technology, the networked control system of electric vehicles has overcome the defects of traditional mechanical and hydraulic structures, such as large volume, slow response, and difficulty in layout, and has improved the structural integration degree and control response speed of the control system. However, the use of in-vehicle network will inevitably introduce signal transmission delay, which will directly affect the system stability of vehicle control, and then affect the safety of vehicle operation, becoming a new challenge for the development of electric vehicle network control technology. Existing networked control methods such as robust
Figure 100002_DEST_PATH_IMAGE002
The infinite control method and the robust gain scheduling control method do not have the ability to explicitly deal with the constraints in the control problem, and cannot fully compensate the adverse effects of the network delay on the control performance, resulting in a decrease in the stability of the controller, all of which have certain limitations. .

模型预测控制算法以其显示处理约束的能力在工业控制领域获得关注，被广泛应用在石油、航空、化工等领域。但冗余繁重的在线计算量对控制单元的计算能力提出了较高要求，增加了车辆的制造成本，同时无法满足车辆控制的实时性要求，这些因素限制了其在电动汽车控制领域的应用。Model predictive control algorithms have gained attention in the field of industrial control because of their ability to display constraints, and have been widely used in petroleum, aviation, chemical and other fields. However, the redundant and heavy on-line calculation puts forward higher requirements on the computing power of the control unit, which increases the manufacturing cost of the vehicle, and cannot meet the real-time requirements of vehicle control. These factors limit its application in the field of electric vehicle control.

如图1所示，为提高车辆动力链的平顺性和稳定性，基于线控技术和车载网络构建了电动汽车网络化动力链控制系统。其原理如下：包括轮速传感器在内的4个传感器采集车辆轮速等状态信息，并通过车载网络反馈给传统动力链控制装置，传统动力链控制装置基于反馈状态信息计算电机转矩控制命令，并通过车载网络将其发送给电机控制单元，调整电机转矩，抑制电传动系振动，从而成为一种典型的网络化控制系统。As shown in Figure 1, in order to improve the smoothness and stability of the vehicle power chain, a networked power chain control system for electric vehicles is constructed based on the wire-controlled technology and on-board network. The principle is as follows: 4 sensors including wheel speed sensors collect status information such as vehicle wheel speed, and feed it back to the traditional powertrain control device through the on-board network. The traditional powertrain control device calculates the motor torque control command based on the feedback status information. And send it to the motor control unit through the in-vehicle network to adjust the motor torque and suppress the vibration of the electric drive train, thus becoming a typical networked control system.

如图2所示，该网络化动力链控制系统，包括动力链控制装置、电机控制单元、电机输出轴转角传感器节点、传动半轴转角传感器节点、轮速传感器节点、电机转速传感器节点，其中电机输出轴转角传感器节点、传动半轴转角传感器节点、轮速传感器节点、电机转速传感器节点与传统动力链控制装置之间通过车载网络相连，构成反馈通道；动力链控制装置通过车载网络又与电机控制单元相连，构成前向通道。根据网络化控制理论，车载网络的使用，将不可避免地引入信号延时，直接影响电动汽车动力链控制的实时性，导致系统扭振失稳，进而影响汽车运行安全，成为电动汽车高性能传动技术发展的新挑战。As shown in Figure 2, the networked powertrain control system includes a powertrain control device, a motor control unit, a motor output shaft angle sensor node, a transmission half-shaft angle sensor node, a wheel speed sensor node, and a motor speed sensor node, among which the motor The output shaft angle sensor node, the transmission half-shaft angle sensor node, the wheel speed sensor node, the motor speed sensor node and the traditional power chain control device are connected through the vehicle network to form a feedback channel; the power chain control device is connected to the motor control device through the vehicle network. The units are connected to form a forward channel. According to the networked control theory, the use of on-board network will inevitably introduce signal delay, which will directly affect the real-time performance of electric vehicle power chain control, resulting in torsional vibration instability of the system, which in turn affects the safety of vehicle operation and becomes a high-performance transmission for electric vehicles. New challenges for technological development.

针对上述挑战，如何充分补偿网络延时对网络化动力链控制系统控制性能的不良影响，增强系统鲁棒性成为关键共性基础技术难题。现有控制方法，如鲁棒增益调度控制方法，不具备显式处理控制问题中约束条件的能力，无法充分补偿网络延时对控制性能的不良影响，致使控制器稳定性降低，均具有一定的局限性。In view of the above challenges, how to fully compensate the adverse effects of network delay on the control performance of the networked powertrain control system and enhance the system robustness has become a key common basic technical problem. Existing control methods, such as robust gain scheduling control methods, do not have the ability to explicitly deal with constraints in control problems, and cannot fully compensate for the adverse effects of network delay on control performance, resulting in reduced controller stability. limitation.

发明内容SUMMARY OF THE INVENTION

本发明的目的在于克服现有网络化控制系统控制装置缺陷，提出一种电动汽车网络化控制系统鲁棒预测控制装置及其控制方法，基于鲁棒预测控制方法，可显式处理约束，充分补偿网络延时对控制系统的不良影响，同时离线设计方案可满足车辆控制的实时性要求。总之，本发明可增强电动汽车网络化控制系统鲁棒性，确保车辆控制稳定性，为提升车辆运行安全提供技术方法支持。The purpose of the present invention is to overcome the defects of the existing networked control system control device, and to propose a robust predictive control device for an electric vehicle networked control system and a control method thereof. Based on the robust predictive control method, constraints can be explicitly processed and sufficient compensation can be achieved. The network delay has an adverse effect on the control system, and the offline design scheme can meet the real-time requirements of vehicle control. In conclusion, the present invention can enhance the robustness of the electric vehicle networked control system, ensure the vehicle control stability, and provide technical method support for improving the vehicle operation safety.

本发明设计了一种电动汽车网络化控制系统鲁棒预测控制装置。该装置包括输入模块、决策模块、输出模块；所述的输入模块、决策模块、输出模块依次相连并且三者协同工作；The invention designs a robust predictive control device for an electric vehicle networked control system. The device includes an input module, a decision-making module, and an output module; the input module, the decision-making module, and the output module are connected in sequence and work together;

其中：in:

输入模块接收来自车载网络传感器节点的实际输入量，根据系统稳态模型得出系统稳态输入量与稳态控制量，并将传感器实际输入量与系统稳态输入量之差以及系统稳态控制量输出到决策模块；The input module receives the actual input from the sensor nodes of the vehicle network, obtains the system steady-state input and the steady-state control value according to the system steady-state model, and calculates the difference between the sensor's actual input and the system's steady-state input and the system steady-state control. output to the decision-making module;

决策模块接收来自输入模块的信息，且基于所接收的车载网络传感器节点实际输入量与系统稳态输入量的差值量，利用离线设计的鲁棒预测控制方法计算系统控制增量，同时将系统控制增量与系统稳态控制量传递到输出模块；The decision-making module receives the information from the input module, and uses the robust predictive control method designed offline to calculate the system control increment based on the received difference between the actual input of the vehicle network sensor node and the steady-state input of the system. The control increment and the system steady-state control quantity are transmitted to the output module;

输出模块接收来自决策模块的信息，且基于接收的系统控制增量与系统稳态控制量，进行求和运算得到电动汽车网络化控制系统控制量，并将其输出给车载网络执行器节点。The output module receives the information from the decision module, and based on the received system control increment and the system steady-state control amount, performs a summation operation to obtain the control amount of the electric vehicle networked control system, and outputs it to the vehicle network actuator node.

上述控制装置的控制方法为，决策模块基于离线设计的鲁棒模型预测控制方法进行构建，以满足网络化电动汽车控制系统的实时性要求。不同于在线设计，离线设计的控制方法可降低计算复杂度，缩短数值优化问题求解时间，其设计步骤包括如下两步：The control method of the above control device is that the decision-making module is constructed based on the robust model predictive control method of offline design, so as to meet the real-time requirements of the networked electric vehicle control system. Different from online design, the control method of offline design can reduce the computational complexity and shorten the numerical optimization problem solving time. The design steps include the following two steps:

（1）求解线性矩阵不等式约束下的线性目标优化问题：定义传感器实际输入量与系统稳态输入量的最大差值为

Figure 100002_DEST_PATH_IMAGE004
，在区间
Figure 100002_DEST_PATH_IMAGE006
上选取间隔相等的
Figure 100002_DEST_PATH_IMAGE008
个点
Figure 100002_DEST_PATH_IMAGE010
，之后针对
Figure 100002_DEST_PATH_IMAGE012
，求解线性矩阵不等式约束下的线性目标优化问题，并将所求得的反馈控制率
Figure 100002_DEST_PATH_IMAGE014
控制在决策模块中；(1) Solve the linear objective optimization problem under the constraints of linear matrix inequality: define the maximum difference between the actual input of the sensor and the steady-state input of the system as
Figure 100002_DEST_PATH_IMAGE004
, in the interval
Figure 100002_DEST_PATH_IMAGE006
select equal intervals
Figure 100002_DEST_PATH_IMAGE008
points
Figure 100002_DEST_PATH_IMAGE010
, then for
Figure 100002_DEST_PATH_IMAGE012
, solve the linear objective optimization problem under the constraints of linear matrix inequality, and use the obtained feedback control rate
Figure 100002_DEST_PATH_IMAGE014
Control is in the decision module;

（2）求解系统控制增量：根据当前传感器实际输入量与系统稳态输入量的差值

Figure 100002_DEST_PATH_IMAGE016
，选择合适的
Figure 100002_DEST_PATH_IMAGE018
使得
Figure 100002_DEST_PATH_IMAGE020
，从而，系统反馈控制率
Figure 100002_DEST_PATH_IMAGE022
表示为：(2) Solving the control increment of the system: according to the difference between the actual input of the current sensor and the steady-state input of the system
Figure 100002_DEST_PATH_IMAGE016
, choose the appropriate
Figure 100002_DEST_PATH_IMAGE018
make
Figure 100002_DEST_PATH_IMAGE020
, so that the system feedback control rate
Figure 100002_DEST_PATH_IMAGE022
Expressed as:

Figure 100002_DEST_PATH_IMAGE024
Figure 100002_DEST_PATH_IMAGE024

系统控制增量

Figure 100002_DEST_PATH_IMAGE026
表示为：System Control Increment
Figure 100002_DEST_PATH_IMAGE026
Expressed as:

Figure 100002_DEST_PATH_IMAGE028
。
Figure 100002_DEST_PATH_IMAGE028
.

步骤（1）中，基于考虑车载网络延时

Figure 100002_DEST_PATH_IMAGE030
的凸多面体模型建立线性矩阵不等式约束下的线性目标优化问题，其具有如下形式：In step (1), based on considering the delay of the in-vehicle network
Figure 100002_DEST_PATH_IMAGE030
The convex polyhedron model of , establishes a linear objective optimization problem under the constraints of linear matrix inequality, which has the following form:

Figure 100002_DEST_PATH_IMAGE032
Figure 100002_DEST_PATH_IMAGE032

其中，

Figure 100002_DEST_PATH_IMAGE034
表示目标函数最小化问题，
Figure 100002_DEST_PATH_IMAGE036
为系统优化变量，
Figure 100002_DEST_PATH_IMAGE038
系统当前时刻传感器实际输入量与系统稳态输入量的差值，
Figure 100002_DEST_PATH_IMAGE040
为系统控制增量最大值，
Figure 100002_DEST_PATH_IMAGE042
为用户指定的系统稳定性相关矩阵；
Figure 100002_DEST_PATH_IMAGE044
，分别为考虑车载网络延时
Figure 100002_DEST_PATH_IMAGE046
的凸多面体模型的系统矩阵顶点以及输入矩阵顶点，
Figure 100002_DEST_PATH_IMAGE048
为凸多面体模型的系统输出矩阵，
Figure 100002_DEST_PATH_IMAGE050
为单位矩阵，上角标
Figure DEST_PATH_IMAGE052
表示向量或矩阵的转置。in,
Figure 100002_DEST_PATH_IMAGE034
represents the objective function minimization problem,
Figure 100002_DEST_PATH_IMAGE036
optimize variables for the system,
Figure 100002_DEST_PATH_IMAGE038
The difference between the actual input of the sensor at the current moment of the system and the steady-state input of the system,
Figure 100002_DEST_PATH_IMAGE040
is the maximum value of the system control increment,
Figure 100002_DEST_PATH_IMAGE042
System stability correlation matrix specified for the user;
Figure 100002_DEST_PATH_IMAGE044
, respectively considering the vehicle network delay
Figure 100002_DEST_PATH_IMAGE046
The system matrix vertices of the convex polyhedron model and the input matrix vertices,
Figure 100002_DEST_PATH_IMAGE048
is the system output matrix of the convex polyhedron model,
Figure 100002_DEST_PATH_IMAGE050
is the identity matrix, superscript
Figure DEST_PATH_IMAGE052
Represents the transpose of a vector or matrix.

所述的考虑车载网络延时

Figure 347319DEST_PATH_IMAGE046
的凸多面体模型，为基于面向电动汽车网络化控制的线性参数时变模型，通过对系统网络延时
Figure 263322DEST_PATH_IMAGE046
的上界
Figure 100002_DEST_PATH_IMAGE055
的准确估计，以泰勒级数展开法建立考虑车载网络延时
Figure 62782DEST_PATH_IMAGE046
的凸多面体模型，其具有如下形式：The described consideration of in-vehicle network delay
Figure 347319DEST_PATH_IMAGE046
The convex polyhedron model is a linear parameter time-varying model based on networked control of electric vehicles.
Figure 263322DEST_PATH_IMAGE046
upper bound
Figure 100002_DEST_PATH_IMAGE055
An accurate estimate of
Figure 62782DEST_PATH_IMAGE046
The convex polyhedron model of , which has the following form:

Figure 100002_DEST_PATH_IMAGE057
Figure 100002_DEST_PATH_IMAGE057

其中，

Figure 100002_DEST_PATH_IMAGE059
表示系统状态，
Figure 100002_DEST_PATH_IMAGE061
表示系统输入，
Figure 100002_DEST_PATH_IMAGE063
表示系统输出，
Figure DEST_PATH_IMAGE065
分别表示系统矩阵、输入矩阵和输出矩阵，
Figure 306813DEST_PATH_IMAGE046
表示车载网络延时，
Figure 100002_DEST_PATH_IMAGE068
分别为考虑车载网络延时
Figure 154814DEST_PATH_IMAGE046
的凸多面体模型的系统矩阵顶点以及输入矩阵顶点。in,
Figure 100002_DEST_PATH_IMAGE059
represents the system state,
Figure 100002_DEST_PATH_IMAGE061
represents system input,
Figure 100002_DEST_PATH_IMAGE063
represents the system output,
Figure DEST_PATH_IMAGE065
represent the system matrix, input matrix and output matrix, respectively,
Figure 306813DEST_PATH_IMAGE046
Indicates the in-vehicle network delay,
Figure 100002_DEST_PATH_IMAGE068
respectively consider the delay of the in-vehicle network
Figure 154814DEST_PATH_IMAGE046
The system matrix vertices of the convex polyhedron model of , as well as the input matrix vertices.

将控制回路中网络诱导延时引入系统控制方程，建立起面向电动汽车网络化控制的线性参数时变模型，可表示为：The network-induced delay in the control loop is introduced into the system control equation, and a linear parameter time-varying model for the networked control of electric vehicles is established, which can be expressed as:

Figure DEST_PATH_IMAGE070
Figure DEST_PATH_IMAGE070

其中，

Figure 863007DEST_PATH_IMAGE059
表示系统状态，
Figure 958002DEST_PATH_IMAGE061
表示系统输入，
Figure 141859DEST_PATH_IMAGE063
表示系统输出，
Figure 121447DEST_PATH_IMAGE065
分别表示系统矩阵、输入矩阵和输出矩阵，
Figure 808781DEST_PATH_IMAGE046
表示车载网络延时。in,
Figure 863007DEST_PATH_IMAGE059
represents the system state,
Figure 958002DEST_PATH_IMAGE061
represents system input,
Figure 141859DEST_PATH_IMAGE063
represents the system output,
Figure 121447DEST_PATH_IMAGE065
represent the system matrix, input matrix and output matrix, respectively,
Figure 808781DEST_PATH_IMAGE046
Indicates the in-vehicle network delay.

本发明的有益效果是：本发明提供的一种电动汽车网络化控制系统鲁棒预测控制装置及其控制方法，可充分补偿网络时滞对系统控制性能的不良影响，提高以低延时为主要特征的车辆预测控制装置实时性能，增强电动汽车网络化控制系统鲁棒性，确保车辆控制稳定性，为设计高性能车辆控制器以及提升车辆运行安全提供技术方法支持。The beneficial effects of the present invention are as follows: the robust predictive control device and the control method of the electric vehicle networked control system provided by the present invention can fully compensate the adverse effects of the network time delay on the control performance of the system, and improve the low delay as the main factor. The characteristic real-time performance of the vehicle predictive control device enhances the robustness of the electric vehicle networked control system, ensures the vehicle control stability, and provides technical method support for designing high-performance vehicle controllers and improving vehicle operation safety.

附图说明Description of drawings

图1为传统电动汽车网络化动力链控制系统控制原理图；Figure 1 is the control principle diagram of the traditional electric vehicle networked power chain control system;

图2为电动汽车网络化动力链控制系统示意图；Figure 2 is a schematic diagram of a networked power chain control system for an electric vehicle;

图3为本发明控制原理图；Fig. 3 is the control principle diagram of the present invention;

图4为本发明输入模块示意图；4 is a schematic diagram of an input module of the present invention;

图5为本发明决策模块示意图；5 is a schematic diagram of a decision-making module of the present invention;

图6为本发明输出模块示意图。FIG. 6 is a schematic diagram of an output module of the present invention.

具体实施方式Detailed ways

下面结合附图进一步详细描述本发明的技术方案，但本发明的保护范围不局限于以下所述。The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

本发明提出了一种电动汽车网络化控制系统鲁棒预测控制装置，既具备显式处理约束条件的能力，又可提高以低延时为主要特征的车辆控制装置实时性能，从而充分抑制网络延时对控制性能的不良影响。将其应用于网络化动力链控制系统，必将改善网络迟滞下动力链控制系统控制性能。The invention proposes a robust predictive control device for a networked control system of an electric vehicle, which not only has the ability to explicitly deal with constraints, but also improves the real-time performance of the vehicle control device with low delay as its main feature, thereby fully suppressing network delays. adverse effects on control performance. Applying it to the networked power chain control system will definitely improve the control performance of the power chain control system under the network hysteresis.

如图3所示，以电动汽车网络化动力链控制为例，说明电动汽车网络化控制系统鲁棒预测控制装置的应用。As shown in Figure 3, the application of the robust predictive control device for the networked control system of the electric vehicle is illustrated by taking the networked power chain control of the electric vehicle as an example.

本发明提出了一种电动汽车网络化控制系统鲁棒预测控制装置，该装置包括输入模块、决策模块、输出模块；所述的输入模块、决策模块、输出模块依次相连并且三者协同工作；The invention provides a robust predictive control device for an electric vehicle networked control system, which includes an input module, a decision-making module, and an output module; the input module, the decision-making module, and the output module are connected in sequence and work cooperatively;

其中，如图4所示，输入模块接收来自网络化动力链控制系统传感器节点的实际输入量，根据系统稳态模型得出系统稳态输入量与稳态控制量，并将传感器实际输入量与系统稳态输入量之差以及系统稳态控制量输出到决策模块；Among them, as shown in Figure 4, the input module receives the actual input from the sensor nodes of the networked power chain control system, obtains the system steady-state input and steady-state control according to the system steady-state model, and compares the actual sensor input with the steady-state control. The difference between the system steady state input quantity and the system steady state control quantity are output to the decision-making module;

如图5所示，决策模块接收来自输入模块的信息，且基于所接收的网络化动力链控制系统传感器节点实际输入量与系统稳态输入量的差值量，利用离线设计的鲁棒预测控制方法计算系统控制增量，同时将系统控制增量与系统稳态控制量传递到输出模块；As shown in Figure 5, the decision-making module receives the information from the input module, and based on the received difference between the actual input of the sensor nodes of the networked power chain control system and the steady-state input of the system, the robust predictive control of offline design is used. The method calculates the system control increment, and transmits the system control increment and the system steady-state control amount to the output module at the same time;

如图6所示，输出模块接收来自决策模块的信息，且基于接收的系统控制增量与系统稳态控制量，进行求和运算得到电动汽车网络化动力链控制系统控制量，并将其输出给网络化动力链控制系统电机控制单元。As shown in Figure 6, the output module receives the information from the decision module, and based on the received system control increment and the system steady-state control amount, the summation operation is performed to obtain the control amount of the electric vehicle networked power chain control system, and the output is Motor control unit for networked powertrain control systems.

上述控制装置的控制方法为，决策模块基于离线设计的鲁棒模型预测控制方法进行构建，以满足网络化电动汽车控制系统的实时性要求。不同于在线设计，离线设计的控制方法可降低计算复杂度，缩短数值优化问题求解时间，其设计步骤包括如下两步：The control method of the above control device is that the decision-making module is constructed based on the robust model predictive control method of offline design, so as to meet the real-time requirements of the networked electric vehicle control system. Different from online design, the control method of offline design can reduce the computational complexity and shorten the numerical optimization problem solving time. The design steps include the following two steps:

（1）求解线性矩阵不等式约束下的线性目标优化问题：定义传感器实际输入量与系统稳态输入量的最大差值为

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，在区间
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上选取间隔相等的
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个点
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，之后针对
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，求解线性矩阵不等式约束下的线性目标优化问题，并将所求得的反馈控制率
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控制在决策模块中；(1) Solve the linear objective optimization problem under the constraints of linear matrix inequality: define the maximum difference between the actual input of the sensor and the steady-state input of the system as
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, in the interval
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select equal intervals
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points
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, then for
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, solve the linear objective optimization problem under the constraints of linear matrix inequality, and use the obtained feedback control rate
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Control is in the decision module;

（2）求解系统控制增量：根据当前传感器实际输入量与系统稳态输入量的差值

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，选择合适的
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使得
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，从而，系统反馈控制率
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表示为：(2) Solving the control increment of the system: according to the difference between the actual input of the current sensor and the steady-state input of the system
Figure 375987DEST_PATH_IMAGE016
, choose the appropriate
Figure 331173DEST_PATH_IMAGE018
make
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, so that the system feedback control rate
Figure 324668DEST_PATH_IMAGE022
Expressed as:

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Figure 783331DEST_PATH_IMAGE024

系统控制增量

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表示为：System Control Increment
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Expressed as:

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。
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.

步骤（1）中，线性矩阵不等式约束下的线性目标优化问题的建立基于考虑车载网络延时

Figure 99540DEST_PATH_IMAGE030
的凸多面体模型，其具有如下形式：In step (1), the establishment of the linear objective optimization problem under the constraints of linear matrix inequality is based on considering the delay of the vehicle network
Figure 99540DEST_PATH_IMAGE030
The convex polyhedron model of , which has the following form:

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Figure 429022DEST_PATH_IMAGE032

其中，

Figure 535518DEST_PATH_IMAGE034
表示目标函数最小化问题，
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为系统优化变量，
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系统当前时刻传感器实际输入量与系统稳态输入量的差值，
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为系统控制增量最大值，
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为用户指定的系统稳定性相关矩阵；
Figure 250161DEST_PATH_IMAGE044
，分别为考虑车载网络延时
Figure 465242DEST_PATH_IMAGE046
的凸多面体模型的系统矩阵顶点以及输入矩阵顶点，
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为凸多面体模型的系统输出矩阵，
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为单位矩阵，上角标
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表示向量或矩阵的转置。in,
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represents the objective function minimization problem,
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optimize variables for the system,
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The difference between the actual input of the sensor at the current moment of the system and the steady-state input of the system,
Figure 747187DEST_PATH_IMAGE040
is the maximum value of the system control increment,
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System stability correlation matrix specified for the user;
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, respectively considering the vehicle network delay
Figure 465242DEST_PATH_IMAGE046
The system matrix vertices of the convex polyhedron model and the input matrix vertices,
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is the system output matrix of the convex polyhedron model,
Figure 164208DEST_PATH_IMAGE050
is the identity matrix, superscript
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Represents the transpose of a vector or matrix.

所述的考虑车载网络延时

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的凸多面体模型，为基于面向电动汽车网络化控制的线性参数时变模型，通过对系统网络延时
Figure 445781DEST_PATH_IMAGE046
的上界
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的准确估计，以泰勒级数展开法建立考虑车载网络延时
Figure 558411DEST_PATH_IMAGE046
的凸多面体模型，其具有如下形式：The described consideration of in-vehicle network delay
Figure 997482DEST_PATH_IMAGE046
The convex polyhedron model is a linear parameter time-varying model based on networked control of electric vehicles.
Figure 445781DEST_PATH_IMAGE046
upper bound
Figure 304147DEST_PATH_IMAGE055
An accurate estimate of
Figure 558411DEST_PATH_IMAGE046
The convex polyhedron model of , which has the following form:

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Figure 654674DEST_PATH_IMAGE057

其中，

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表示系统状态，
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表示系统输入，
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表示系统输出，
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分别表示系统矩阵、输入矩阵和输出矩阵，
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表示车载网络延时，
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分别为考虑车载网络延时
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的凸多面体模型的系统矩阵顶点以及输入矩阵顶点。in,
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represents the system state,
Figure 540907DEST_PATH_IMAGE061
represents system input,
Figure 349595DEST_PATH_IMAGE063
represents the system output,
Figure 549632DEST_PATH_IMAGE065
represent the system matrix, input matrix and output matrix, respectively,
Figure 542996DEST_PATH_IMAGE046
Indicates the in-vehicle network delay,
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respectively consider the delay of the in-vehicle network
Figure 440861DEST_PATH_IMAGE046
The system matrix vertices of the convex polyhedron model of , as well as the input matrix vertices.

将控制回路中网络诱导延时引入系统控制方程，建立起面向电动汽车网络化动力链控制的线性参数时变模型，可表示为：The network-induced delay in the control loop is introduced into the system control equation, and a linear parameter time-varying model for the networked power chain control of electric vehicles is established, which can be expressed as:

Figure 511717DEST_PATH_IMAGE070
Figure 511717DEST_PATH_IMAGE070

其中，

Figure 675982DEST_PATH_IMAGE059
表示系统状态，
Figure 448766DEST_PATH_IMAGE061
表示系统输入，
Figure 599256DEST_PATH_IMAGE063
表示系统输出，
Figure 305043DEST_PATH_IMAGE065
分别表示系统矩阵、输入矩阵和输出矩阵，
Figure 453259DEST_PATH_IMAGE046
表示车载网络延时。in,
Figure 675982DEST_PATH_IMAGE059
represents the system state,
Figure 448766DEST_PATH_IMAGE061
represents system input,
Figure 599256DEST_PATH_IMAGE063
represents the system output,
Figure 305043DEST_PATH_IMAGE065
represent the system matrix, input matrix and output matrix, respectively,
Figure 453259DEST_PATH_IMAGE046
Indicates the in-vehicle network delay.

得益于模型预测控制方法固有的显式处理约束的能力以及离线设计方案特有的控制实时性优势，本发明可充分补偿网络时滞对系统控制性能的不良影响，提高以低延时为主要特征的车辆预测控制装置实时性能，增强电动汽车网络化控制系统鲁棒性，确保车辆控制稳定性，为设计高性能车辆控制器以及提升车辆运行安全提供技术方法支持。Benefiting from the inherent explicit processing constraint capability of the model predictive control method and the unique control real-time advantage of the offline design scheme, the present invention can fully compensate the adverse effects of the network time delay on the system control performance, and improve the low delay as the main feature. The real-time performance of the vehicle predictive control device is improved, the robustness of the electric vehicle networked control system is enhanced, the vehicle control stability is ensured, and technical method support is provided for designing high-performance vehicle controllers and improving vehicle operation safety.

以上所述仅为本发明的一个具体实例，本发明不仅仅局限于上述实施例，凡在本发明的精神和原则之内所做的局部性改动、等同替换、改进等均应包含在本发明的保护范围之内。The above is only a specific example of the present invention, and the present invention is not limited to the above-mentioned embodiments. All local changes, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the present invention. within the scope of protection.

Claims (4)

1.电动汽车网络化控制系统鲁棒预测控制装置的控制方法，所述的电动汽车网络化控制系统鲁棒预测控制装置，包括输入模块、决策模块、输出模块；所述的输入模块、决策模块、输出模块依次相连并且三者协同工作；1. A control method for a robust predictive control device for an electric vehicle networked control system, the robust predictive control device for an electric vehicle networked control system includes an input module, a decision-making module, and an output module; the input module and the decision-making module , the output modules are connected in sequence and the three work together; 所述的输入模块接收来自车载网络传感器节点的实际输入量，根据系统稳态模型得出系统稳态输入量与稳态控制量，并将传感器实际输入量与系统稳态输入量之差以及系统稳态控制量输出到决策模块；The input module receives the actual input from the sensor node of the vehicle network, obtains the system steady-state input and steady-state control according to the system steady-state model, and calculates the difference between the sensor's actual input and the system's steady-state input and the system's steady-state input. The steady-state control quantity is output to the decision-making module; 所述的决策模块接收来自输入模块的信息，且基于所接收的车载网络传感器节点实际输入量与系统稳态输入量的差值量，利用离线设计的鲁棒预测控制方法计算系统控制增量，同时将系统控制增量与系统稳态控制量传递到输出模块；The decision-making module receives the information from the input module, and based on the received difference between the actual input of the vehicle network sensor node and the steady-state input of the system, the robust predictive control method of offline design is used to calculate the system control increment, At the same time, the system control increment and the system steady-state control amount are transmitted to the output module; 所述的输出模块接收来自决策模块的信息，且基于接收的系统控制增量与系统稳态控制量，进行求和运算得到电动汽车网络化控制系统控制量，并将其输出给车载网络执行器节点；The output module receives the information from the decision-making module, and based on the received system control increment and the system steady-state control amount, performs a summation operation to obtain the control amount of the electric vehicle networked control system, and outputs it to the vehicle network actuator. node; 其特征在于：所述的决策模块基于离线设计的鲁棒模型预测控制方法进行构建；It is characterized in that: the decision-making module is constructed based on the robust model predictive control method of offline design; 决策模块设计步骤包括如下两步：The design steps of the decision module include the following two steps: （1）求解线性矩阵不等式约束下的线性目标优化问题：定义传感器实际输入量与系统稳态输入量的最大差值为
Figure DEST_PATH_IMAGE002
，在区间
Figure DEST_PATH_IMAGE004
上选取间隔相等的
Figure DEST_PATH_IMAGE006
个点
Figure DEST_PATH_IMAGE008
，之后针对
Figure DEST_PATH_IMAGE010
，求解线性矩阵不等式约束下的线性目标优化问题，并将所求得的反馈控制率
Figure DEST_PATH_IMAGE012
控制在决策模块中；(1) Solve the linear objective optimization problem under the constraints of linear matrix inequality: define the maximum difference between the actual input of the sensor and the steady-state input of the system as
Figure DEST_PATH_IMAGE002
, in the interval
Figure DEST_PATH_IMAGE004
select equal intervals
Figure DEST_PATH_IMAGE006
points
Figure DEST_PATH_IMAGE008
, then for
Figure DEST_PATH_IMAGE010
, solve the linear objective optimization problem under the constraints of linear matrix inequality, and use the obtained feedback control rate
Figure DEST_PATH_IMAGE012
Control is in the decision module; （2）求解系统控制增量：根据当前传感器实际输入量与系统稳态输入量的差值
Figure DEST_PATH_IMAGE014
，选择合适的
Figure DEST_PATH_IMAGE016
使得
Figure DEST_PATH_IMAGE018
，从而，系统反馈控制率
Figure DEST_PATH_IMAGE020
表示为：(2) Solving the control increment of the system: according to the difference between the actual input of the current sensor and the steady-state input of the system
Figure DEST_PATH_IMAGE014
, choose the appropriate
Figure DEST_PATH_IMAGE016
make
Figure DEST_PATH_IMAGE018
, so that the system feedback control rate
Figure DEST_PATH_IMAGE020
Expressed as:
Figure DEST_PATH_IMAGE022
Figure DEST_PATH_IMAGE022
 系统控制增量
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表示为：System Control Increment
Figure DEST_PATH_IMAGE024
Expressed as:
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。
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. 2.根据权利要求1所述的电动汽车网络化控制系统鲁棒预测控制装置的控制方法，其特征在于：步骤（1）中，基于考虑车载网络延时
Figure DEST_PATH_IMAGE028
的凸多面体模型建立线性矩阵不等式约束下的线性目标优化问题，其具有如下形式：2 . The control method of the robust predictive control device for an electric vehicle networked control system according to claim 1 , wherein: in step (1), based on the consideration of the on-board network delay
Figure DEST_PATH_IMAGE028
The convex polyhedron model of , establishes a linear objective optimization problem under the constraints of linear matrix inequality, which has the following form:
Figure DEST_PATH_IMAGE030
Figure DEST_PATH_IMAGE030
 其中，
Figure DEST_PATH_IMAGE032
表示目标函数最小化问题，
Figure DEST_PATH_IMAGE034
为系统优化变量，
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系统当前时刻传感器实际输入量与系统稳态输入量的差值，
Figure DEST_PATH_IMAGE038
为系统控制增量最大值，
Figure DEST_PATH_IMAGE040
为用户指定的系统稳定性相关矩阵；
Figure DEST_PATH_IMAGE042
，分别为考虑车载网络延时
Figure DEST_PATH_IMAGE044
的凸多面体模型的系统矩阵顶点以及输入矩阵顶点，
Figure DEST_PATH_IMAGE046
为凸多面体模型的系统输出矩阵，
Figure DEST_PATH_IMAGE048
为单位矩阵，上角标
Figure DEST_PATH_IMAGE050
表示向量或矩阵的转置。in,
Figure DEST_PATH_IMAGE032
represents the objective function minimization problem,
Figure DEST_PATH_IMAGE034
optimize variables for the system,
Figure DEST_PATH_IMAGE036
The difference between the actual input of the sensor at the current moment of the system and the steady-state input of the system,
Figure DEST_PATH_IMAGE038
is the maximum value of the system control increment,
Figure DEST_PATH_IMAGE040
System stability correlation matrix specified for the user;
Figure DEST_PATH_IMAGE042
, respectively considering the vehicle network delay
Figure DEST_PATH_IMAGE044
The system matrix vertices of the convex polyhedron model and the input matrix vertices,
Figure DEST_PATH_IMAGE046
is the system output matrix of the convex polyhedron model,
Figure DEST_PATH_IMAGE048
is the identity matrix, superscript
Figure DEST_PATH_IMAGE050
Represents the transpose of a vector or matrix. 3.根据权利要求2所述的电动汽车网络化控制系统鲁棒预测控制装置的控制方法，其特征在于：所述的考虑车载网络延时
Figure 617779DEST_PATH_IMAGE044
的凸多面体模型，为基于面向电动汽车网络化控制的线性参数时变模型，通过对系统网络延时
Figure 485984DEST_PATH_IMAGE044
的上界
Figure DEST_PATH_IMAGE053
的准确估计，以泰勒级数展开法建立考虑车载网络延时
Figure 937826DEST_PATH_IMAGE044
的凸多面体模型，其具有如下形式：3. The control method of the robust predictive control device for an electric vehicle networked control system according to claim 2, characterized in that: the described consideration of on-board network delay
Figure 617779DEST_PATH_IMAGE044
The convex polyhedron model is a linear parameter time-varying model based on networked control of electric vehicles.
Figure 485984DEST_PATH_IMAGE044
upper bound
Figure DEST_PATH_IMAGE053
An accurate estimate of
Figure 937826DEST_PATH_IMAGE044
The convex polyhedron model of , which has the following form:
Figure DEST_PATH_IMAGE055
Figure DEST_PATH_IMAGE055
 其中，
Figure DEST_PATH_IMAGE057
表示系统状态，
Figure DEST_PATH_IMAGE059
表示系统输入，
Figure DEST_PATH_IMAGE061
表示系统输出，
Figure DEST_PATH_IMAGE063
分别表示系统矩阵、输入矩阵和输出矩阵，
Figure 83767DEST_PATH_IMAGE044
表示车载网络延时，
Figure DEST_PATH_IMAGE066
分别为考虑车载网络延时
Figure 881828DEST_PATH_IMAGE044
的凸多面体模型的系统矩阵顶点以及输入矩阵顶点。in,
Figure DEST_PATH_IMAGE057
represents the system state,
Figure DEST_PATH_IMAGE059
represents system input,
Figure DEST_PATH_IMAGE061
represents the system output,
Figure DEST_PATH_IMAGE063
represent the system matrix, input matrix and output matrix, respectively,
Figure 83767DEST_PATH_IMAGE044
Indicates the in-vehicle network delay,
Figure DEST_PATH_IMAGE066
respectively consider the delay of the in-vehicle network
Figure 881828DEST_PATH_IMAGE044
The system matrix vertices of the convex polyhedron model of , as well as the input matrix vertices. 4.根据权利要求3所述的电动汽车网络化控制系统鲁棒预测控制装置的控制方法，其特征在于：将控制回路中网络诱导延时引入系统控制方程，建立起面向电动汽车网络化控制的线性参数时变模型，表示为：4. The control method of the robust predictive control device of an electric vehicle networked control system according to claim 3, characterized in that: the network-induced delay in the control loop is introduced into the system control equation, and a network-oriented control system for electric vehicles is established. A time-varying model with linear parameters, expressed as:
Figure DEST_PATH_IMAGE068
Figure DEST_PATH_IMAGE068
 其中，
Figure 796301DEST_PATH_IMAGE057
表示系统状态，
Figure 328913DEST_PATH_IMAGE059
表示系统输入，
Figure 731076DEST_PATH_IMAGE061
表示系统输出，
Figure DEST_PATH_IMAGE069
分别表示系统矩阵、输入矩阵和输出矩阵，
Figure 321326DEST_PATH_IMAGE044
表示车载网络延时。in,
Figure 796301DEST_PATH_IMAGE057
represents the system state,
Figure 328913DEST_PATH_IMAGE059
represents system input,
Figure 731076DEST_PATH_IMAGE061
represents the system output,
Figure DEST_PATH_IMAGE069
represent the system matrix, input matrix and output matrix, respectively,
Figure 321326DEST_PATH_IMAGE044
Indicates the in-vehicle network delay.
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