CN110161864A - A kind of large dead time Fuzzy control system - Google Patents
A kind of large dead time Fuzzy control system Download PDFInfo
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- CN110161864A CN110161864A CN201910507562.8A CN201910507562A CN110161864A CN 110161864 A CN110161864 A CN 110161864A CN 201910507562 A CN201910507562 A CN 201910507562A CN 110161864 A CN110161864 A CN 110161864A
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- 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
- G05B13/042—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 in which a parameter or coefficient is automatically adjusted to optimise the performance
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Abstract
The present invention provides a kind of large dead time Fuzzy control systems, including Proctor Central, fuzzy controller, System Discrimination module, modified result module, fuzzy model identification module；The Proctor Central output is connected to fuzzy controller and System Discrimination module, the fuzzy controller output is connected to modified result module and System Discrimination module, the System Discrimination module output is connected to modified result module and fuzzy model identification module, the modified result module output is connected to control object, and the fuzzy model identification module output is connected to module controller.The present invention passes through the setting of the modules such as Proctor Central, fuzzy controller, System Discrimination module, modified result module, fuzzy model identification module, control system can be made to construct new fuzzy model automatically when encountering control environmental change, to make Fuzzy control system can adapt to more complicated environment, versatility significantly improves, and then greatly extends the application range of fuzzy model identification building fuzzy model.
Description
Technical field
The present invention relates to a kind of large dead time Fuzzy control systems.
Background technique
In the prior art use fuzzy control Correction for Large Dead Time System, frequently with neural network fuzzy control or based on L-K it is general
The T-S fuzzy control of letter, although be able to achieve nonlinear Control, and from mathematical algorithm for stability it is secure, and be based on mould
Fuzzy model identification can solve automatic building fuzzy model, but in actual Control System Design, be based on inputoutput data
Building fuzzy model analyzes fuzzy model using L-K functional, needs just to have before constructing fuzzy model enough
Data, once fuzzy model comes into operation, then can only in desired extent appropriate adjustment, and be unable to ensure system in control ring
Border constructs new fuzzy model when changing automatically, this significantly limits the application model of fuzzy model identification building fuzzy model
It encloses.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of large dead time Fuzzy control system, the large dead time Fuzzy Control
System processed passes through the moulds such as Proctor Central, fuzzy controller, System Discrimination module, modified result module, fuzzy model identification module
Block setting can make control system construct new fuzzy model automatically when encountering control environmental change.
The present invention is achieved by the following technical programs.
A kind of large dead time Fuzzy control system provided by the invention, including Proctor Central, fuzzy controller, System Discrimination mould
Block, modified result module, fuzzy model identification module；The Proctor Central output is connected to fuzzy controller and System Discrimination mould
Block, the fuzzy controller output are connected to modified result module and System Discrimination module, and the System Discrimination module output is connected to
Modified result module and fuzzy model identification module, the modified result module output are connected to control object, the fuzzy model
Identification module output is connected to module controller.
The Proctor Central input terminal receives the status information of control object, is judged whether there is according to the control information of input
It is abnormal, there is exception then to send abnormal marking to System Discrimination module and sends status information to fuzzy controller, it is without exception then
Status information is sent to fuzzy controller according to preset bounds segmentation；Proctor Central will be sent to the status information of fuzzy controller
It is sent to System Discrimination module together.
The fuzzy controller carries out Fuzzy Calculation with built-in fuzzy model, and Fuzzy Calculation result is sent to system simultaneously
Recognize module and modified result module.
The System Discrimination module calculates control deviation according to status information and Fuzzy Calculation result, and control deviation is less than threshold
Value then calculates adjustment data according to control deviation and is sent to modified result module, and control deviation such as larger than threshold value is then by status information
Fuzzy model identification module is sent to the calculating of Fuzzy Calculation result.
The modified result module is modified calculating to Fuzzy Calculation result according to the adjustment data of caching, and will amendment
Calculated result is sent to control object.
The fuzzy model identification module carries out fuzzy model according to the status information and Fuzzy Calculation result in multiple periods
Identification, and the built-in blur model in fuzzy controller is updated according to fuzzy model identification result.
The fuzzy model identification result of the fuzzy model identification module is sent to Modifying model module, Modifying model module
It is the existing fuzzy model of new fuzzy model or caching according to the judgement of fuzzy model identification result, new fuzzy model is then first in this way
Fuzzy model identification is completed in multiple timing to fuzzy model identification module after caching to be again sent to complete fuzzy model
The existing fuzzy model of caching is then directly sent to fuzzy controller by fuzzy controller, the existing fuzzy model cached in this way.
The Modifying model module calculates abnormal judgment threshold also according to the fuzzy model for being sent to fuzzy controller, will
Abnormal judgment threshold is sent to Proctor Central.
The beneficial effects of the present invention are: pass through Proctor Central, fuzzy controller, System Discrimination module, modified result mould
The setting of the modules such as block, fuzzy model identification module can be such that control system constructs automatically newly when encountering control environmental change
Fuzzy model, so that Fuzzy control system be made to can adapt to more complicated environment, versatility is significantly improved, and then is greatly extended
The application range of fuzzy model identification building fuzzy model.
Detailed description of the invention
Fig. 1 is connection schematic diagram of the invention.
Specific embodiment
Be described further below technical solution of the present invention, but claimed range be not limited to it is described.
A kind of large dead time Fuzzy control system as shown in Figure 1, including Proctor Central, fuzzy controller, System Discrimination mould
Block, modified result module, fuzzy model identification module；The Proctor Central output is connected to fuzzy controller and System Discrimination mould
Block, the fuzzy controller output are connected to modified result module and System Discrimination module, and the System Discrimination module output is connected to
Modified result module and fuzzy model identification module, the modified result module output are connected to control object, the fuzzy model
Identification module output is connected to module controller.
The Proctor Central input terminal receives the status information of control object, is judged whether there is according to the control information of input
It is abnormal, there is exception then to send abnormal marking to System Discrimination module and sends status information to fuzzy controller, it is without exception then
Status information is sent to fuzzy controller according to preset bounds segmentation；Proctor Central will be sent to the status information of fuzzy controller
It is sent to System Discrimination module together.
The fuzzy controller carries out Fuzzy Calculation with built-in fuzzy model, and Fuzzy Calculation result is sent to system simultaneously
Recognize module and modified result module.
The System Discrimination module calculates control deviation according to status information and Fuzzy Calculation result, and control deviation is less than threshold
Value then calculates adjustment data according to control deviation and is sent to modified result module, and control deviation such as larger than threshold value is then by status information
Fuzzy model identification module is sent to the calculating of Fuzzy Calculation result.
The modified result module is modified calculating to Fuzzy Calculation result according to the adjustment data of caching, and will amendment
Calculated result is sent to control object.
The fuzzy model identification module carries out fuzzy model according to the status information and Fuzzy Calculation result in multiple periods
Identification, and the built-in blur model in fuzzy controller is updated according to fuzzy model identification result.
The fuzzy model identification result of the fuzzy model identification module is sent to Modifying model module, Modifying model module
It is the existing fuzzy model of new fuzzy model or caching according to the judgement of fuzzy model identification result, new fuzzy model is then first in this way
Fuzzy model identification is completed in multiple timing to fuzzy model identification module after caching to be again sent to complete fuzzy model
The existing fuzzy model of caching is then directly sent to fuzzy controller by fuzzy controller, the existing fuzzy model cached in this way.
The Modifying model module calculates abnormal judgment threshold also according to the fuzzy model for being sent to fuzzy controller, will
Abnormal judgment threshold is sent to Proctor Central.
Substantially there are three types of working conditions by the present invention as a result:
1, normal condition: control environment is in desired extent, and the received status information of Proctor Central without abnormal, distinguish by system
The control deviation that knowledge module is calculated is again smaller than threshold value, and fuzzy model identification module and Modifying model module do not work at this time,
Whole realized using fuzzy controller and modified result module as core of system controls；
2, adjust state: control environment changes, but variation degree is little, and the received status information of Proctor Central is not different
Often, but the control deviation that is calculated of System Discrimination module is also greater than threshold value, at this time fuzzy model identification module and Modifying model
Module enters working condition, is modified to the fuzzy model parameter of fuzzy controller, and system is whole still with fuzzy controller
It is that core realizes control with modified result module；
3, construct state: control environment has great variety, and the received status information of Proctor Central is abnormal, System Discrimination at this time
Module does not need to calculate control deviation, and system is whole using fuzzy model identification module and Modifying model module as core, and building is new
Fuzzy model, fuzzy controller is controlled before receiving new fuzzy model with original fuzzy model at this time, but by
Do not calculate control deviation in System Discrimination module, thus on the process nature of fuzzy control be it is metastable, it is new convenient for building
Fuzzy model.
In general, such as L-K functional analysis model is calculated in System Discrimination module, high performance chips are utilized
Computing capability violence calculates analysis model, therefore analysis model is according to practical control environment when being arranged, be contemplated that variation because
Control environment correction term is added in element.
Claims (8)
1. a kind of large dead time Fuzzy control system, including Proctor Central, fuzzy controller, System Discrimination module, modified result mould
Block, fuzzy model identification module, it is characterised in that: the Proctor Central output is connected to fuzzy controller and System Discrimination module,
The fuzzy controller output is connected to modified result module and System Discrimination module, and the System Discrimination module output is connected to result
Correction module and fuzzy model identification module, the modified result module output are connected to control object, the fuzzy model identification
Module output is connected to module controller.
2. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the Proctor Central input terminal receives control
The status information of object processed judges whether there is exception according to the control information of input, has abnormal then to the transmission of System Discrimination module
Abnormal marking simultaneously sends status information to fuzzy controller, without exception, and status information is sent to according to preset bounds segmentation
Fuzzy controller；The status information for being sent to fuzzy controller is sent to System Discrimination module by Proctor Central together.
3. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the fuzzy controller is with built-in blur
Model carries out Fuzzy Calculation, and Fuzzy Calculation result is sent to System Discrimination module and modified result module simultaneously.
4. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the System Discrimination module is according to state
Information and Fuzzy Calculation result calculate control deviation, and control deviation is less than threshold value and then calculates adjustment data transmission according to control deviation
Modified result module is given, status information and the calculating of Fuzzy Calculation result are then sent to fuzzy model by control deviation such as larger than threshold value
Recognize module.
5. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the modified result module is according to caching
Adjustment data calculating is modified to Fuzzy Calculation result, and corrected Calculation result is sent to control object.
6. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the fuzzy model identification module according to
The status information and Fuzzy Calculation result in multiple periods carry out fuzzy model identification, and according to fuzzy model identification result to fuzzy
Built-in blur model in controller is updated.
7. large dead time Fuzzy control system as described in claim 1, it is characterised in that: the mould of the fuzzy model identification module
Fuzzy model identification result is sent to Modifying model module, and Modifying model module is new fuzzy according to the judgement of fuzzy model identification result
The existing fuzzy model that model still caches, after new fuzzy model then first caches in this way when fuzzy model identification module when multiple
Fuzzy model identification is completed in sequence, and complete fuzzy model is sent to fuzzy controller again, the existing fuzzy model cached in this way
The existing fuzzy model of caching is directly then sent to fuzzy controller.
8. large dead time Fuzzy control system as claimed in claim 7, it is characterised in that: the Modifying model module is also according to hair
It send to the fuzzy model of fuzzy controller, calculates abnormal judgment threshold, abnormal judgment threshold is sent to Proctor Central.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113568305A (en) * | 2021-06-10 | 2021-10-29 | 贵州恰到科技有限公司 | Control method of deep reinforcement learning model robot |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527841A (en) * | 2015-12-10 | 2016-04-27 | 北京联合大学 | Networking tracking control method of time-varying signal |
| CN105867125A (en) * | 2016-04-07 | 2016-08-17 | 中国中化股份有限公司 | Optimization control method and apparatus of refining apparatus coupling unit |
| CN106227042A (en) * | 2016-08-31 | 2016-12-14 | 马占久 | Dissolved oxygen control method based on fuzzy neural network |
| CN107143649A (en) * | 2017-05-26 | 2017-09-08 | 合肥工业大学 | A kind of congestion industry and mining city and fluid drive gearshift update the system and its method |
| CN108733955A (en) * | 2018-05-30 | 2018-11-02 | 厦门大学 | A kind of intelligent electric automobile longitudinal movement control system and method |
-
2019
- 2019-06-12 CN CN201910507562.8A patent/CN110161864A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105527841A (en) * | 2015-12-10 | 2016-04-27 | 北京联合大学 | Networking tracking control method of time-varying signal |
| CN105867125A (en) * | 2016-04-07 | 2016-08-17 | 中国中化股份有限公司 | Optimization control method and apparatus of refining apparatus coupling unit |
| CN106227042A (en) * | 2016-08-31 | 2016-12-14 | 马占久 | Dissolved oxygen control method based on fuzzy neural network |
| CN107143649A (en) * | 2017-05-26 | 2017-09-08 | 合肥工业大学 | A kind of congestion industry and mining city and fluid drive gearshift update the system and its method |
| CN108733955A (en) * | 2018-05-30 | 2018-11-02 | 厦门大学 | A kind of intelligent electric automobile longitudinal movement control system and method |
Non-Patent Citations (5)
| Title |
|---|
| 刘福才,等: "基于T-S模糊模型的模型参考自适应逆控制", 《系统工程与电子技术》 * |
| 孙庚山,等: "《工程模糊控制》", 30 November 1995 * |
| 李雷，等: "基于T-S模糊神经网络的南京市水质评价方法研究", 《计量与测试技术》 * |
| 肖会芹,等: "基于T-S模糊模型的采样数据网络控制系统H_∞输出跟踪控制", 《自动化学报》 * |
| 衣法臻,等: "基于大时滞特性预估补偿的模型辨识及模糊控制方法", 《北京交通大学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113568305A (en) * | 2021-06-10 | 2021-10-29 | 贵州恰到科技有限公司 | Control method of deep reinforcement learning model robot |
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