LU501679B1 - Truetime2.0-based simulation modeling method for aero-engine distributed control system - Google Patents

Truetime2.0-based simulation modeling method for aero-engine distributed control system Download PDF

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Publication number
LU501679B1
LU501679B1 LU501679A LU501679A LU501679B1 LU 501679 B1 LU501679 B1 LU 501679B1 LU 501679 A LU501679 A LU 501679A LU 501679 A LU501679 A LU 501679A LU 501679 B1 LU501679 B1 LU 501679B1
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block
engine
intelligent
aero
intelligent sensor
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LU501679A
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German (de)
Inventor
Wenxiang Zhou
Muxuan Pan
Jinquan Huang
Yiwei Li
Yun Xu
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Univ Nanjing Aeronautics & Astronautics
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Programmable Controllers (AREA)

Abstract

The present disclosure discloses a TrueTime2.0-based simulation modeling method for aero-engine distributed control system, comprising the following steps: Step 1), building an aero-engine simulation model; Step 2), building an aero-engine intelligent sensor model; Step 5 3), building an intelligent actuator model; Step 4), building a distributed controller model; Step 5), building a simulation model of engine distributed control system based on the TrueTime models of engine, intelligent sensor, intelligent actuator and controller. The present disclosure aims to construct engine distributed control system on the premise of aero-engine distributed control framework, playing a positive role in promoting the research on the distributed control system for intelligent engines, more electric engines and other advanced aero-engines in the future.

Description

BL-5446
TRUETIME2.0-BASED SIMULATION MODELING METHOD FOR AERO-ENGINE -U501679
DISTRIBUTED CONTROL SYSTEM
TECHNICAL FIELD
[01] The present disclosure is included in the technical field of aero-engine control, and relates to a TrueTime2.0-based simulation modeling method for aero-engine distributed control system.
BACKGROUND ART
[02] With the continuous development of aero-engine in aerothermodynamics and machinery, its performance and efficiency have reached an unprecedented level. Accordingly, the aero-engine control is also developing constantly. Compared with the traditional centralized control system, the aero-engine distributed control system can significantly reduce the weight of control system and improve the reliability and maintainability of system. It is necessary to study the modeling method with a view to better study the distributed control system. TrueTime is a toolbox developed based on Simulink. It can be used to build a dynamic process of real- time distributed control system and a joint simulation environment to control the execution of task and interact with network, and study the simulation of various scheduling strategies and network protocol multi-control system performance, allowing easier research on network control system.
SUMMARY
[03] For the above-described issue, the present disclosure proposes a TrueTime2.0-based simulation modeling method for aero-engine distributed control system. The models of multi- intelligent sensor and multi-intelligent actuator are built through TrueTime2.0 taking into account the aero-engine distributed control system, and connected with the controller by the communication protocol. The controller is equipped with multiple receive blocks to receive the data from the corresponding intelligent sensor. The intelligent sensor and intelligent actuator are connected with the aero-engine model. The intelligent sensor is used to detect the state of engine and send the data to the controller. After receiving the data from the intelligent sensor and calculating the corresponding control quantity, the controller sends the data to the intelligent actuator which will return the received data into the engine model.
[04] The present disclosure proposes a TrueTime2.0-based simulation modeling method for aero-engine distributed control system, comprising the following content: building the simulation models of aero-engine, intelligent sensor, controller, intelligent actuator and
TrueTime2.0-based distributed control system.
[05] Step 1.1): use Matlab Function block through Simulink toolbox in Matlab;
[06] Step 1.2): use the existing dynamic link library through Function block to build the aero-engine simulation model,
[07] Step 2.1): use Kernel block in TrueTime2.0 to establish a data receive block of the intelligent sensor for each intelligent sensor;
[08] Step 2.2): use Kernel block in TrueTime2.0 to establish a data buffer block of the 1
BL-5446 intelligent sensor for each intelligent sensor;
LU501679
[09] Step 2.3): set the parameters of the intelligent sensor’s receive Kernel block in the “Block parameters: Turetime Kernel” dialog box ofthe intelligent sensor’s receive Kernel block.
And set the initialization file of the intelligent sensor’s receive Kernel block and name it as “Sensor_init.m” in the field “Name of init function(MEX or MATLAB)”; set [1 0] in the field “Number of analog inputs and outputs”, which indicates the number of analog inputs and outputs of the intelligent sensor is 1 and 0, respectively; and set the network number of the intelligent sensor in the field “(Network and) Node number(s)”;
[10] Step 2.4): initialize the intelligent sensor’s receive Kernel block. In the initialization file of the intelligent sensor’s receive Kernel block, set the scheduling mode of the intelligent sensor’s receive Kernel block as “deadline-monotonic scheduling” , and define the start time of working of the intelligent sensor as ts1, the cycle of sending data from the intelligent sensor to the network as Tsl, and the signal processing file name of the intelligent sensor as “Sensor code.m”. According to these parameters, create the periodic task of the intelligent sensor using the “ttCreatPeriodicTask()” function;
[11] Step 2.5): create a “Sensor code.m” file. In this file, edit the data reading code of the intelligent sensor’s A/D interface and the code to send the data into the corresponding intelligent sensor’s data buffer block;
[12] Step 2.6): set the parameters of the intelligent sensor’s buffer Kernel block in the “Block parameters: Turetime Kernel” dialog box of the intelligent sensor’s buffer Kernel block, and create the initialization file of the buffer block and name it as “Buff initm”; set [0 1] in the field “Number of analog inputs and outputs”, which indicates the number of analog inputs and outputs of the intelligent sensor is 0 and 1, respectively; and set the network number of this intelligent sensor’s buffer block in the field “(Network and) Node number(s)”;
[13] Step 2.7): initialize the intelligent sensor’s buffer Kernel block: in the initialization file “Buff init.m” of the intelligent sensor’s buffer Kernel block, set the scheduling method of the intelligent sensor’s buffer Kernel block as “deadline-monotonic scheduling”, define the intelligent sensor data buffer file name as “Buff code.m”, and use the ttCreateTask() function to create aperiodic tasks, to enable the intelligent sensor’s buffer block to receive the data sent by the intelligent sensor, without working periodically;
[14] Step 2.8): create the file “Buff code.m”. In this file, the “ttGetMsg” function is used to write the codes for reading data from the network. After the data are judged as non-empty, they will be output via the D/A interface of intelligent sensor’s buffer Kernel block;
[15] Step 3.1): create the controller’s Kernel block by using the Kernel block of TrueTime 2.0;
[16] Step 3.2): set the parameters of the controller’s Kernel block in the “Block parameters:
Turetime Kernel” dialog box of the controller’s Kernel block, and create the initialization file of the controller’s Kernel block and name it as “Controller _init.m” in the field “Name of init function(MEX or MATLAB)”; set [ncin ncout] in the field “Number of analog inputs and outputs”, which indicates the number of analog inputs and outputs of the controller is ncin and ncout, respectively; and set the network number of the controller’s buffer block in the field “(Network and) Node number(s)”; 2
BL-5446
[17] Step 3.3): initialize the controller’s receive Kernel block: in the initialization file “Controller _init.m” of the controller’s receive Kernel block, set the scheduling method of the LU501679 controller’s receive Kernel block as “deadline-monotonic scheduling”, define the initial working time of the controller as ts2, the cycle for the controller to send data to the network as
Ts2, and the controller code file name as “Controller code.m”. Based on these parameters, use the “ttCreatPeriodicTask()” function to create periodic tasks for the controller. In particular, a data structure “data” is to be provided in the function to save the parameters required by the controller code. This data structure represents the local memory of the task and all data required in the controller code is prefixed with this data structure name;
[18] Step 3.4): create the file “Controller code.m”. In this file, the code for reading the controller’s A/D interface data is written, and the parameters set in Step 3.3) and the PID control method are applied to calculate the control variables, which are then sent to the intelligent actuator;
[19] Step 4.1): use the Kernel block of TrueTime2.0 to establish an intelligent actuator network node block for each intelligent actuator;
[20] Step 4.2): set the parameters of the intelligent actuator’s Kernel block in the “Block parameters: Turetime Kernel” dialog box of the intelligent actuator’s Kernel block. and create the initialization file of the intelligent actuator’s Kernel block and name it as “Actuator _init.m” in the field “Name of init function(MEX or MATLAB)”; set [0 1] in the field “Number of analog inputs and outputs”, which indicates the number of analog inputs and outputs of the controller is 0 and 1, respectively; and set the network number of this intelligent actuator block in the field “(Network and) Node number(s)”;
[21] Step 4.3): initialize the intelligent actuator’s Kernel block: in the initialization file “Actuator init. m” of the intelligent actuator’s Kernel block, set the scheduling method of the intelligent actuator’s Kernel block as “deadline-monotonic scheduling”, define the intelligent actuator code file name as “Actuator _code.m”, and use the ttCreateTask() function to create aperiodic tasks, to enable the intelligent actuator block to receive the data sent by the controller, without working periodically;
[22] Step 4.4): create the file “Actuator code.m”. In this file, the “ttGetMsg” function is used to write the codes for reading data from the network. After the data are judged as non- empty, they will be output via the D/A interface of intelligent actuator’s Kernel block;
[23] Step 5.1): use the Network block in the toolbox of TrueTime2.0 to establish the network characteristic model of engine distributed control system;
[24] Step 5.2): connect the output of the engine simulation model’s EngineFcn block to the intelligent sensor’s receive Kernel block, the output of the intelligent sensor’s buffer Kernel block to the multiplexing block, the reference command input block (Simulink step block, etc.) to the multiplexing block (mux), and the output of the intelligent actuator’s Kernel block to the input of the engine model’s Simulink block;
[25] Step 5.3): according to the total number of network nodes n in the engine distributed control system, set the “Number of nodes” in the “Block Parameters: TrueTime Network” field to n, which indicates that the number of network nodes in the engine distributed control network is n; 3
BL-5446
[26] Step 5.4): in “Block Parameters: TrueTime Network”, set the “Static schedule” field to [No. of intelligent sensor’s receive Kernel block No. of controller’s Kernel], which LU501679 indicates the network scheduling strategy of the engine distributed control system;
[27] Step 5.5): in “Block Parameters: TrueTime Network”, set the parameters in the
FrameSize and DateRate fields, which indicate the frame size and the speed of network data transmission, respectively.
BRIEF DESCRIPTION OF THE DRAWINGS
[28] FIG. 1 shows the TrueTime2.0 Toolbox.
[29] FIG. 2 shows the Kernel Configuration.
[30] FIG. 3 shows the Network Configuration.
[31] FIG. 4 shows the Distributed Control System Model.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[32] The present disclosure will be further described with reference to drawings and embodiment.
[33] The TrueTime2.0 toolbox is as shown in FIG. 1. In this embodiment, mainly Kernel and Network blocks are used, in which the Kernel block is used to simulate intelligent sensor, intelligent actuator and controller, and the Network block is used to set up network parameters.
The Kernel configuration is as shown in FIG. 2. FIG. 3 shows the Network configuration; for
TDMA(TTP/C), the static schedule is added at the bottom row of Network block parameters.
[34] As described in Step 1, use Matlab Function block of Simulink to call the existing dynamic link library in the Function block, and take engine high pressure rotor speed lu and engine pressure ratio (EPR) as engine output, and fuel oil Wr and engine nozzle area A as engine input to build aero-engine simulation model.
[35] As described in Step 2, in the “Block parameters: TrueTime Kernel” as shown in FIG. 2, set the node numbers of intelligent sensor’s receive blocks 1 and 2 as 1, 2 in the field “(Network and) Node number(s)”, and set the analog inputs and outputs as [1 0] in the field “Number of analog inputs and outputs”. With intelligent sensor’s receive block 1 taken as an example for writing the initialization file and code of intelligent sensor, name the initialization file as ‘Sensorl init’, and make corresponding change in the settings of intelligent sensor 1 to realize consistent initialization file name. First of all, use ‘ttInitKernel (‘prioDM’)’ statement to complete the design of Kernel scheduling method, and complete this setting in the remaining
Kernel initialization as well. It is required to create a periodic task so that the intelligent sensor periodically detects engine signals. Therefore, use ttCreatePeriodicTask in the initialization file of the intelligent sensor to create the periodic task, and set the starttime and period of the task as Os and 0.02 s respectively as needed, which means that the task will start at 0 s and last for a period of 0.02 s. At the same time, call with statement the intelligent sensor code ‘Sensorl code’. 4
BL-5446
[36] Set the node numbers of buffer blocks 1 and 2 as 3 and 4 respectively, and set the analog inputs and outputs as [0 1]. With buffer block 1 taken as an example for writing the LUS01679 initialization file and code of buffer block, use Matlab language to name the initialization file as ‘Buffl init’, and make corresponding change in the settings of buffer block 1 to realize consistent initialization file name. As the buffer block receives the data sent by intelligent sensor, which is an aperiodic task, it is required to use ttCreateTask and ttAttachNetworkHandler to create the aperiodic task. Both tasknames created using ttCreateTask and ttAttachNetworkHandler shall be the same. The task will start when the message sent by intelligent sensor arrives at the receive block. When creating the task, set the relative deadline ofthe task as 0.02 s, and set in ttCreateTask the code of buffer block 1 as ‘Buff] code’.
[37] As described in Step 3, in the “Block parameters: TrueTime Kernel” as shown in FIG. 2, set the node number of controller as 5 in the field “(Network and) Node number(s)”, and set the analog inputs and outputs as [5 0] in the field “Number of analog inputs and outputs”.
Similar to writing the intelligent sensor, when writing the controller block, use
TTCreatePeriodicTask to create a periodic task which will start at 0 s and last for a period of 0.02 s as needed. In particular, due to the use of PID control algorithm in controller code, the data structure is specially required to be added in the process of the creation of periodic task. In this embodiment, as the data structure name is ‘data’, the ‘data’ shall be added as a prefix to the proportional, integral and derivative parameters required for the algorithm in controller code, e.g ‘data. K1’ as proportional parameter. The initialization file also requires the completion of initialization of algorithm-related parameters. These related parameters shall also be added with a prefix so that the data with a prefix can be transferred to code and stored.
[38] As described in Step 4, in the “Block parameters: TrueTime Kernel” as shown in FIG. 2, set the node numbers of intelligent actuator as 6, 7 in the field “(Network and) Node number(s)” , and set the analog inputs and outputs as [0 1] in the field “Number of analog inputs and outputs”. The intelligent actuator, with a similar function as intelligent sensor’s buffer block, reads from the network the data sent by controller. At this point, the models of intelligent sensor, controller and intelligent actuator in the distributed control system are all completely built and set up.
[39] As described in Step 5, use the Network block of TrueTime to connect the engine model’s output port with the intelligent sensor’s receive block, connect the input port with the intelligent actuator, and connect the controller with the intelligent sensor’s buffer block. In this embodiment, 7 Kernels are used, the communication network is TDMA, the frame size is set at 80 bits, the data transfer rate is 80,000 bits/s, the static schedule is [1 5;2 5], and a corresponding change is made in the Network block based on these settings. At this point, the models of intelligent sensor, controller and intelligent actuator in the distributed control system are all completely built and set up. The final TrueTime-based aero-engine distributed control system model is as shown in FIG. 4. 5

Claims (2)

BL-5446 CLAIMS LU501679
1. À TrueTime-based simulation modeling method for aero-engine distributed control system, characterized in that it builds the simulation models of aero-engine, intelligent sensor, controller, intelligent actuator and TrueTime2.0-based aero-engine distributed control system.
2. A TrueTime-based simulation modeling method for aero-engine distributed control system according to claim 1, characterized in that: Step 1: establish EngineFcn block through Function block in Simulink toolbox in Matlab; and call the dynamic link library of aero-engine component-level models in EngineFcn block to build the aero-engine simulation model, Step 2: use Kernel block of TrueTime2.0 to establish a data receive Kernel block and an data buffer block of intelligent sensor for each intelligent sensor; and set the parameters of the intelligent sensor’s receive Kernel block. Then initialize the intelligent sensor’s receive Kernel block; create the “Sensor code.m” file and edit the code for sending the data to the corresponding intelligent sensor’s data buffer block; set the parameters of the intelligent sensor’s buffer Kernel block; initialize the intelligent sensor’s buffer Kernel block; create a “Buff code.m” file to output the data from the D/A interface of the intelligent sensor’s buffer Kernel block after reading data from the network and judging that it is non null, Step 3: establish the controller’s Kernel block and set the parameters of the controller’s Kernel block; initialize the controller’s receive Kernel block; create a “Controller code.m” file to send the data into the intelligent actuator after reading the code from the A/D interface data and calculating the control quantity, Step 4: build a network node model for each intelligent actuator, set and initialize the parameters, and create corresponding file to read the code, Step 5: build a network characteristic model of distributed control system for engines, connect the blocks correctly, and set the number of nodes and the block parameters; set the parameters in the fields of FrameSize and DateRate of “Block Parameters: TrueTime Network”, indicating the frame size and transmission speed of network data respectively. 6
LU501679A 2022-03-17 2022-03-17 Truetime2.0-based simulation modeling method for aero-engine distributed control system LU501679B1 (en)

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Application Number Priority Date Filing Date Title
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