CN101916121A - Gas flow control method - Google Patents
Gas flow control method Download PDFInfo
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- CN101916121A CN101916121A CN 201010234227 CN201010234227A CN101916121A CN 101916121 A CN101916121 A CN 101916121A CN 201010234227 CN201010234227 CN 201010234227 CN 201010234227 A CN201010234227 A CN 201010234227A CN 101916121 A CN101916121 A CN 101916121A
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Abstract
The invention relates to a method for controlling gas flow by using a computer. The method comprises the following steps of: setting target flow in the computer, and inputting the target flow, feedback flow of a controlled object and output feedback of a PID controller to a DRNN (diagonal recurrent neural network) together; inputting the target flow to the PID controller after algorithm processing of the DRNN, and taking the target flow and the output feedback of the PID controller together as the input of the PID controller; after processing of the PID controller and D/A conversion, amplifying the input and dividing the input into two paths of signals, and inputting one path of signal to an electric valve to control the opening of an air flue; inputting the other path of signals to a variable frequency motor to drive a blower to generate certain air quantity to enter an APU cabin through the air flue; and detecting the air quantity by using the sensor, and taking the detection result as the feedback input of next cycle. The gas flow control method realizes high-speed accurate control of gas flow by combining the DRNN control algorithm and the PID control principle, and has wide application prospect.
Description
Technical field
The invention belongs to the computer control field, relate to a kind of method of utilizing the computer control gas flow.
Background technology
Fire extinguishing system is made of fire-suppression bottle, sensor, pipeline, nozzle in the aircraft.It is to guarantee still can carry out the important system of safe flight under the situation of aircraft prominent chance fire in flight course.
In certain type airplane design process,, need carry out the original reason test of fire extinguishing system in order to guarantee rationalization, the optimization of fire extinguishing system design.
The simulation of air flow system is the key equipment of aircraft fire suppression system original reason test, and its major function is at the trial, for the APU cabin provides continual and steady air-flow.
What prior art was taked air-flow control is classical PID control, by behind ratio, integration and the differential of PID processing module result and the desired value that obtains being compared, but, because the complicacy of native system and the physical characteristics of gas itself, classical PID is controlled at precision and ageing aspect is slightly inadequate.
Summary of the invention
The objective of the invention is, provide a kind of can be efficiently, the accurate method of pilot-gas flow.
Technical solution of the present invention is, a kind of flow rate controlling method, and its step is as follows:
Step 2 neural network is handled, and enters the PID controller through the algorithm process of DRNN neural network, with the input as the PID controller of the output feedback of PID controller;
Step 3PID handles, through producing the input to controlled device after the comparison process of PID controller;
The output of step 4PID controller is through the D/A conversion, and the generation simulating signal enters servo power amplifier and carries out power amplification;
Step 5 is divided into two paths of signals after amplifying, and one the tunnel enters motorized valve, by the aperture in motorized valve control air channel; Another road input variable-frequency motor, the variable-frequency motor belt driven blower produces certain air quantity and enters the APU cabin by the air channel;
Step 6 is detected the air channel air quantity by sensor, with testing result input signal conditioning device, enters computer control system after the A/D conversion, and imports as the feedback of next round.
Described DRNN Processing with Neural Network process in the step 2 is as follows: the feedback flow of target flow, controlled device and the output of PID controller feedback are as 3 input layers of DRNN neural network, algorithm computing through input layer, produce output and enter each hidden node, hidden layer has the self feed back function, algorithm computing through hidden layer, the output that produces is pooled to output layer, through the algorithm computing of output layer, produces the output of DRNN neural network.
The weights of described input layer, hidden layer, output layer adopt the gradient optimizing algorithm that drives quantifier.
The DI/O output switching value of described computing machine drives to relay, drives the switch control of carrying out variable-frequency motor via relay.
The invention has the beneficial effects as follows: flow rate controlling method of the present invention is introduced the DRNN neural network control method on the basis of classical PID control theory, by emulation and evidence, the present invention is than classical PID control, in response time of control, reach stable time and the overshoot of control all has remarkable advantages, in the extinguishing property original reason test of APU cabin, obtained good application, help to improve the aerial extinguishing performance, improve flight safety, have broad application prospects.
Description of drawings
Fig. 1 is the process flow diagram of flow rate controlling method of the present invention;
Fig. 2 is the schematic diagram of the signal processing function module in the computing machine for the present invention;
Fig. 3 is the test control effect contrast figure of the present invention and classical PID.
Embodiment
The present invention is further illustrated below by embodiment:
In certain type aircraft APU cabin fire extinguishing system original reason test, use the present invention gas flow is controlled.See also Fig. 1, it is the process flow diagram of flow rate controlling method of the present invention.Its simulation of air flow system is made up of TT﹠C system and gas path device.Wherein, the main computing machine of described TT﹠C system (main frame, display, mouse, keyboard, printer), observing and controlling passage (A/D passage, D/A passage, D/IO passage, signal conditioner, relay driver), servo actuator and sensor are formed, wherein, be provided with the signal processing function module in the described main frame.And gas path device mainly is made up of motorized valve, variable-frequency motor, fan blower and air channel.Wherein, A/D passage, D/A passage, D/IO passage all link to each other with computing machine.Described D/A passage is exported after servo effect is joined with motorized valve and frequency conversion drive simultaneously from computing machine.Control fan blower and air channel then, thereby the pilot piping flow is realized at last to air-flow control in the APU cabin.Described D/IO passage drives with switch control through relay and links to each other, by the control of this switch control realization to frequency conversion drive.And sensor is used to monitor the piping flow in air channel, is handled by computing machine behind signal conditioner and A/D passage then.
Please consult Fig. 2 simultaneously, it is the schematic diagram of the signal processing function module in the computing machine.Described signal processing function module is made up of set-point, DRNN neural network, PID controller and controlled device four parts.
Wherein, set-point partly is meant target setting flow in computing machine, the feedback flow of target flow, controlled device and the output of PID controller feedback.The gradient optimizing algorithm that the DRNN neural network comprises input layer, hidden layer, output layer and acts on the three, wherein, described hidden layer has the delay feedback of an output.Described PID controller can compare data of collecting and given target flow, then this difference is used to calculate output valve, and the purpose of this output valve is can allow the flow of controlled device reach or remain on target flow.
Algorithm process through the DRNN neural network enters the PID controller, with the input as the PID controller of the output feedback of PID controller, handles the back through the PID controller and produces input to controlled device.The feedback flow of described target flow, controlled device and the output of PID controller feedback are as 3 input layers of DRNN neural network, algorithm computing through input layer, produce output and enter each hidden node, hidden layer has the self feed back function, algorithm computing through hidden layer, the output that produces is pooled to output layer, produces the output of DRNN neural network through the algorithm computing of output layer, and enters the PID controller again.Wherein, the weights of described input layer, hidden layer, output layer adopt the gradient optimizing algorithm that drives quantifier.
And described PID controller output produces simulating signal and enters servo power amplifier through the D/A conversion.Be divided into two paths of signals after the amplification, the one tunnel enters motorized valve, by the aperture in motorized valve control air channel; Another road input variable-frequency motor, the variable-frequency motor belt driven blower produces certain air quantity and enters the APU cabin by the air channel.By sensor the air channel air quantity is detected, testing result input signal conditioning device is entered computer control system after the A/D conversion.Drive to relay by DI/O output switching value, drive the switch control of carrying out variable-frequency motor via relay.
Provide the step of flow rate controlling method of the present invention below:
Step 2 neural network is handled, and enters the PID controller through the algorithm process of DRNN neural network, with the input as the PID controller of the output feedback of PID controller;
Step 3PID handles, and handles the input of back generation to controlled device through the PID controller;
The output of step 4PID controller produces simulating signal and enters servo power amplifier, i.e. power amplifier through the D/A conversion;
Step 5 is divided into two paths of signals after amplifying, and one the tunnel enters motorized valve, by the aperture in motorized valve control air channel; Another road input variable-frequency motor, the variable-frequency motor belt driven blower produces certain air quantity and enters the APU cabin by the air channel;
Step 6 is detected the air channel air quantity by sensor, with testing result input signal conditioning device, enters computer control system after the A/D conversion, and imports as the feedback of next round.
During test, when setting a certain target flow, behind the signal processing function module arithmetic, the result is affacted on the gas path device by observing and controlling passage, servo actuator.The real-time traffic of gas path device feeds back to the signal processing function module by sensor acquisition, observing and controlling passage simultaneously, and the signal processing function module is exported through the control that produces a new round after the comparison operation according to feedback flow and target flow.So repeatedly, reach and remain on target flow up to the feedback flow.The initial value of feedback flow is 0.
See also Fig. 3, it is the test control effect contrast figure of the present invention and classical PID.By seeing among the figure, reaching of first control is about 0.4 of classical PID control stabilization time, and overshoot is about 0.7 of classical PID control, and therefore with respect to the PID control of prior art, it is controlled at precision and ageing equal tool improves a lot.
Flow rate controlling method of the present invention has in sum been realized the high speed of gas flow, accurately control by in conjunction with DRNN ANN (Artificial Neural Network) Control algorithm and PID control principle.By emulation and evidence, the present invention is than classical PID control, in response time of control, reach stable time and the overshoot of control all has remarkable advantages.Flow rate controlling method of the present invention is by the principle research to jet system in the cabin, in the extinguishing property original reason test of APU cabin, obtained good application, help to improve the aerial extinguishing performance, improve flight safety, have broad application prospects.
Claims (4)
1. a flow rate controlling method is characterized in that, its step is as follows:
Step 1 initialization, target setting flow in computing machine, the feedback flow of target flow, controlled device and the output of PID controller feedback enter the DRNN neural network together;
Step 2 neural network is handled, and enters the PID controller through the algorithm process of DRNN neural network, with the input as the PID controller of the output feedback of PID controller;
Step 3PID handles, through producing the input to controlled device after the comparison process of PID controller;
The output of step 4PID controller is through the D/A conversion, and the generation simulating signal enters servo power amplifier and carries out power amplification;
Step 5 is divided into two paths of signals after amplifying, and one the tunnel enters motorized valve, by the aperture in motorized valve control air channel; Another road input variable-frequency motor, the variable-frequency motor belt driven blower produces certain air quantity and enters the APU cabin by the air channel;
Step 6 is detected the air channel air quantity by sensor, with testing result input signal conditioning device, enters computer control system after the A/D conversion, and imports as the feedback of next round.
2. flow rate controlling method according to claim 1, it is characterized in that: in the step 2, described DRNN Processing with Neural Network process is as follows: the feedback flow of target flow, controlled device and the output of PID controller feedback are as 3 input layers of DRNN neural network, algorithm computing through input layer, produce output and enter each hidden node, hidden layer has the self feed back function, algorithm computing through hidden layer, the output that produces is pooled to output layer, through the algorithm computing of output layer, produce the output of DRNN neural network.
3. flow rate controlling method according to claim 2 is characterized in that: the weights of described input layer, hidden layer, output layer adopt the gradient optimizing algorithm that drives quantifier.
4. flow rate controlling method according to claim 3 is characterized in that: the DI/O output switching value of described computing machine drives to relay, drives the switch control of carrying out variable-frequency motor via relay.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049008A (en) * | 2011-10-14 | 2013-04-17 | 东京毅力科创株式会社 | Flow rate controller and processing apparatus |
| CN103197693A (en) * | 2013-04-09 | 2013-07-10 | 安徽省安光环境光学工程技术研究中心有限公司 | Air mass flow rate control device |
| CN104102237A (en) * | 2014-07-08 | 2014-10-15 | 北京七星华创电子股份有限公司 | Regulating valve characteristic optimization method for mass flow controller |
| CN106155120A (en) * | 2016-09-08 | 2016-11-23 | 中国航空工业集团公司西安飞机设计研究所 | A kind of multichannel flow allocation method and multichannel flow distributing system |
| CN109143842A (en) * | 2018-07-25 | 2019-01-04 | 江苏拙术智能制造有限公司 | A kind of Wiring harness connector welding equipment control system based on PID control |
| CN109778106A (en) * | 2018-11-14 | 2019-05-21 | 苏州工业园区姑苏科技有限公司 | A kind of control system and adjusting method of the n-formyl sarcolysine alcohol protective atmosphere of meshbeltfurnace |
| CN112906875A (en) * | 2021-04-29 | 2021-06-04 | 常州高凯电子有限公司 | Control system and method for precise gas flow valve |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103049008A (en) * | 2011-10-14 | 2013-04-17 | 东京毅力科创株式会社 | Flow rate controller and processing apparatus |
| CN103197693A (en) * | 2013-04-09 | 2013-07-10 | 安徽省安光环境光学工程技术研究中心有限公司 | Air mass flow rate control device |
| CN103197693B (en) * | 2013-04-09 | 2016-08-17 | 安徽省安光环境光学工程技术研究中心有限公司 | A kind of MAF controls device |
| CN104102237A (en) * | 2014-07-08 | 2014-10-15 | 北京七星华创电子股份有限公司 | Regulating valve characteristic optimization method for mass flow controller |
| CN104102237B (en) * | 2014-07-08 | 2016-09-07 | 北京七星华创电子股份有限公司 | A kind of regulation valve characteristic optimization method of mass flow controller |
| CN106155120A (en) * | 2016-09-08 | 2016-11-23 | 中国航空工业集团公司西安飞机设计研究所 | A kind of multichannel flow allocation method and multichannel flow distributing system |
| CN109143842A (en) * | 2018-07-25 | 2019-01-04 | 江苏拙术智能制造有限公司 | A kind of Wiring harness connector welding equipment control system based on PID control |
| CN109778106A (en) * | 2018-11-14 | 2019-05-21 | 苏州工业园区姑苏科技有限公司 | A kind of control system and adjusting method of the n-formyl sarcolysine alcohol protective atmosphere of meshbeltfurnace |
| CN112906875A (en) * | 2021-04-29 | 2021-06-04 | 常州高凯电子有限公司 | Control system and method for precise gas flow valve |
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| CN101916121B (en) | 2011-12-21 |
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