CN110161864A - A kind of large dead time Fuzzy control system - Google Patents
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Abstract
本发明提供了一种大时滞模糊控制系统，包括预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块；所述预控制器输出接至模糊控制器和系统辨识模块，所述模糊控制器输出接至结果修正模块和系统辨识模块，所述系统辨识模块输出接至结果修正模块和模糊模型辨识模块，所述结果修正模块输出接至控制对象，所述模糊模型辨识模块输出接至模块控制器。本发明通过预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块等模块设置，能够使控制系统在遇到控制环境变化时自动构建新的模糊模型，从而使模糊控制系统能够适应更复杂的环境，通用性显著提高，进而极大的扩展模糊模型辨识构建模糊模型的应用范围。
The present invention provides a large time-delay fuzzy control system, including a pre-controller, a fuzzy controller, a system identification module, a result correction module, and a fuzzy model identification module; the output of the pre-controller is connected to the fuzzy controller and the system identification module , the output of the fuzzy controller is connected to the result correction module and the system identification module, the output of the system identification module is connected to the result correction module and the fuzzy model identification module, the output of the result correction module is connected to the control object, and the fuzzy model identification The module output is connected to the module controller. The present invention can enable the control system to automatically build a new fuzzy model when encountering changes in the control environment through the pre-controller, fuzzy controller, system identification module, result correction module, fuzzy model identification module and other modules, so that the fuzzy control system It can adapt to more complex environments, and its versatility is significantly improved, thereby greatly expanding the application range of fuzzy model identification and construction of fuzzy models.
Description
技术领域technical field
本发明涉及一种大时滞模糊控制系统。The invention relates to a large time-delay fuzzy control system.
背景技术Background technique
现有技术中采用模糊控制的大时滞系统，常采用神经网络模糊控制或基于L-K泛函的T-S模糊控制，虽然能实现非线性控制，且从数学算法上而言稳定性有保障，而基于模糊模型辨识能解决自动构建模糊模型，但是在实际的控制系统设计中，基于输入输出数据构建模糊模型、利用L-K泛函对模糊模型进行分析，需要在构建模糊模型之前就具备足够多的数据，一旦模糊模型投入使用，则只能在预期范围内适当调整，而无法确保系统在控制环境变化时自动构建新的模糊模型，这极大的限制了模糊模型辨识构建模糊模型的应用范围。The large time-delay system using fuzzy control in the prior art often adopts neural network fuzzy control or T-S fuzzy control based on L-K functional. Fuzzy model identification can solve the problem of automatically building fuzzy models, but in actual control system design, building fuzzy models based on input and output data, and using L-K functionals to analyze fuzzy models requires sufficient data before building fuzzy models. Once the fuzzy model is put into use, it can only be properly adjusted within the expected range, but cannot ensure that the system automatically builds a new fuzzy model when the control environment changes, which greatly limits the application range of fuzzy model identification and construction of fuzzy models.
发明内容Contents of the invention
为解决上述技术问题，本发明提供了一种大时滞模糊控制系统，该大时滞模糊控制系统通过预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块等模块设置，能够使控制系统在遇到控制环境变化时自动构建新的模糊模型。In order to solve the above technical problems, the present invention provides a large time-delay fuzzy control system, the large time-delay fuzzy control system is set by modules such as a pre-controller, a fuzzy controller, a system identification module, a result correction module, and a fuzzy model identification module. , which enables the control system to automatically build a new fuzzy model when encountering changes in the control environment.
本发明通过以下技术方案得以实现。The present invention is achieved through the following technical solutions.
本发明提供的一种大时滞模糊控制系统，包括预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块；所述预控制器输出接至模糊控制器和系统辨识模块，所述模糊控制器输出接至结果修正模块和系统辨识模块，所述系统辨识模块输出接至结果修正模块和模糊模型辨识模块，所述结果修正模块输出接至控制对象，所述模糊模型辨识模块输出接至模块控制器。The present invention provides a large time-delay fuzzy control system, including a pre-controller, a fuzzy controller, a system identification module, a result correction module, and a fuzzy model identification module; the output of the pre-controller is connected to the fuzzy controller and the system identification module , the output of the fuzzy controller is connected to the result correction module and the system identification module, the output of the system identification module is connected to the result correction module and the fuzzy model identification module, the output of the result correction module is connected to the control object, and the fuzzy model identification The module output is connected to the module controller.
所述预控制器输入端接收控制对象的状态信息，根据输入的控制信息判断是否有异常，有异常则向系统辨识模块发送异常标记、并向模糊控制器发送状态信息，无异常则将状态信息按照预设界限分段发送至模糊控制器；预控制器将发送至模糊控制器的状态信息一并发送至系统辨识模块。The input terminal of the pre-controller receives the state information of the control object, and judges whether there is an abnormality according to the input control information. If there is an abnormality, an abnormal mark is sent to the system identification module, and the state information is sent to the fuzzy controller. If there is no abnormality, the state information is sent to the fuzzy controller. Segmentally send to the fuzzy controller according to the preset limit; the pre-controller sends the state information sent to the fuzzy controller to the system identification module.
所述模糊控制器以内置模糊模型进行模糊计算，将模糊计算结果同时发送至系统辨识模块和结果修正模块。The fuzzy controller performs fuzzy calculation with a built-in fuzzy model, and sends the fuzzy calculation result to the system identification module and the result correction module at the same time.
所述系统辨识模块根据状态信息和模糊计算结果计算控制偏差，控制偏差小于阈值则根据控制偏差计算调整数据发送给结果修正模块，控制偏差如大于阈值则将状态信息和模糊计算结果计算发送至模糊模型辨识模块。The system identification module calculates the control deviation according to the state information and the fuzzy calculation result. If the control deviation is less than the threshold, the adjustment data is calculated according to the control deviation and sent to the result correction module. If the control deviation is greater than the threshold, the state information and the fuzzy calculation result are calculated and sent to the fuzzy Model Identification Module.
所述结果修正模块根据缓存的调整数据对模糊计算结果进行修正计算，并将修正计算结果发送至控制对象。The result correction module performs correction calculation on the fuzzy calculation result according to the cached adjustment data, and sends the correction calculation result to the control object.
所述模糊模型辨识模块根据多个周期的状态信息和模糊计算结果进行模糊模型辨识，并根据模糊模型辨识结果对模糊控制器中的内置模糊模型进行更新。The fuzzy model identification module performs fuzzy model identification according to the state information of multiple cycles and fuzzy calculation results, and updates the built-in fuzzy model in the fuzzy controller according to the fuzzy model identification results.
所述模糊模型辨识模块的模糊模型辨识结果发送至模型修正模块，模型修正模块根据模糊模型辨识结果判断是新模糊模型还是缓存的已有模糊模型，如是新模糊模型则先缓存后待模糊模型辨识模块在多个时序中完成模糊模型辨识再将完整的模糊模型发送至模糊控制器，如是缓存的已有模糊模型则直接将缓存的已有模糊模型发送至模糊控制器。The fuzzy model identification result of the fuzzy model identification module is sent to the model correction module, and the model correction module judges whether it is a new fuzzy model or a cached existing fuzzy model according to the fuzzy model identification result. The module completes fuzzy model identification in multiple time series and then sends the complete fuzzy model to the fuzzy controller. If it is a cached existing fuzzy model, it directly sends the cached existing fuzzy model to the fuzzy controller.
所述模型修正模块还根据发送至模糊控制器的模糊模型，计算异常判断阈值，将异常判断阈值发送至预控制器。The model correction module also calculates the abnormality judgment threshold according to the fuzzy model sent to the fuzzy controller, and sends the abnormality judgment threshold to the pre-controller.
本发明的有益效果在于：通过预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块等模块设置，能够使控制系统在遇到控制环境变化时自动构建新的模糊模型，从而使模糊控制系统能够适应更复杂的环境，通用性显著提高，进而极大的扩展模糊模型辨识构建模糊模型的应用范围。The beneficial effects of the present invention are: through the pre-controller, fuzzy controller, system identification module, result correction module, fuzzy model identification module and other module settings, the control system can automatically build a new fuzzy model when encountering changes in the control environment, Therefore, the fuzzy control system can adapt to more complex environments, and the versatility is significantly improved, thereby greatly expanding the application range of fuzzy model identification and construction of fuzzy models.
附图说明Description of drawings
图1是本发明的连接示意图。Fig. 1 is a connection diagram of the present invention.
具体实施方式Detailed ways
下面进一步描述本发明的技术方案，但要求保护的范围并不局限于所述。The technical solution of the present invention is further described below, but the scope of protection is not limited to the description.
如图1所示的一种大时滞模糊控制系统，包括预控制器、模糊控制器、系统辨识模块、结果修正模块、模糊模型辨识模块；所述预控制器输出接至模糊控制器和系统辨识模块，所述模糊控制器输出接至结果修正模块和系统辨识模块，所述系统辨识模块输出接至结果修正模块和模糊模型辨识模块，所述结果修正模块输出接至控制对象，所述模糊模型辨识模块输出接至模块控制器。A kind of large time-delay fuzzy control system as shown in Figure 1, comprises pre-controller, fuzzy controller, system identification module, result correction module, fuzzy model identification module; Described pre-controller output is connected to fuzzy controller and system Identification module, the output of the fuzzy controller is connected to the result correction module and the system identification module, the output of the system identification module is connected to the result correction module and the fuzzy model identification module, the output of the result correction module is connected to the control object, and the fuzzy The output of the model identification module is connected to the module controller.
所述预控制器输入端接收控制对象的状态信息，根据输入的控制信息判断是否有异常，有异常则向系统辨识模块发送异常标记、并向模糊控制器发送状态信息，无异常则将状态信息按照预设界限分段发送至模糊控制器；预控制器将发送至模糊控制器的状态信息一并发送至系统辨识模块。The input terminal of the pre-controller receives the state information of the control object, and judges whether there is an abnormality according to the input control information. If there is an abnormality, an abnormal mark is sent to the system identification module, and the state information is sent to the fuzzy controller. If there is no abnormality, the state information is sent to the fuzzy controller. Segmentally send to the fuzzy controller according to the preset limit; the pre-controller sends the state information sent to the fuzzy controller to the system identification module.
所述模糊控制器以内置模糊模型进行模糊计算，将模糊计算结果同时发送至系统辨识模块和结果修正模块。The fuzzy controller performs fuzzy calculation with a built-in fuzzy model, and sends the fuzzy calculation result to the system identification module and the result correction module at the same time.
所述系统辨识模块根据状态信息和模糊计算结果计算控制偏差，控制偏差小于阈值则根据控制偏差计算调整数据发送给结果修正模块，控制偏差如大于阈值则将状态信息和模糊计算结果计算发送至模糊模型辨识模块。The system identification module calculates the control deviation according to the state information and the fuzzy calculation result. If the control deviation is less than the threshold, the adjustment data is calculated according to the control deviation and sent to the result correction module. If the control deviation is greater than the threshold, the state information and the fuzzy calculation result are calculated and sent to the fuzzy Model Identification Module.
所述结果修正模块根据缓存的调整数据对模糊计算结果进行修正计算，并将修正计算结果发送至控制对象。The result correction module performs correction calculation on the fuzzy calculation result according to the cached adjustment data, and sends the correction calculation result to the control object.
所述模糊模型辨识模块根据多个周期的状态信息和模糊计算结果进行模糊模型辨识，并根据模糊模型辨识结果对模糊控制器中的内置模糊模型进行更新。The fuzzy model identification module performs fuzzy model identification according to the state information of multiple cycles and fuzzy calculation results, and updates the built-in fuzzy model in the fuzzy controller according to the fuzzy model identification results.
所述模糊模型辨识模块的模糊模型辨识结果发送至模型修正模块，模型修正模块根据模糊模型辨识结果判断是新模糊模型还是缓存的已有模糊模型，如是新模糊模型则先缓存后待模糊模型辨识模块在多个时序中完成模糊模型辨识再将完整的模糊模型发送至模糊控制器，如是缓存的已有模糊模型则直接将缓存的已有模糊模型发送至模糊控制器。The fuzzy model identification result of the fuzzy model identification module is sent to the model correction module, and the model correction module judges whether it is a new fuzzy model or a cached existing fuzzy model according to the fuzzy model identification result. The module completes fuzzy model identification in multiple time series and then sends the complete fuzzy model to the fuzzy controller. If it is a cached existing fuzzy model, it directly sends the cached existing fuzzy model to the fuzzy controller.
所述模型修正模块还根据发送至模糊控制器的模糊模型，计算异常判断阈值，将异常判断阈值发送至预控制器。The model correction module also calculates the abnormality judgment threshold according to the fuzzy model sent to the fuzzy controller, and sends the abnormality judgment threshold to the pre-controller.
由此，本发明实质上有三种工作状态：Thereby, the present invention has three working states substantially:
1、正常状态：控制环境在预期范围内，预控制器接收的状态信息没有异常，系统辨识模块计算得到的控制偏差也小于阈值，此时模糊模型辨识模块和模型修正模块不工作，系统整体以模糊控制器和结果修正模块为核心实现控制；1. Normal state: the control environment is within the expected range, the state information received by the pre-controller is normal, and the control deviation calculated by the system identification module is also less than the threshold. At this time, the fuzzy model identification module and model correction module do not work, and the system as a whole Fuzzy controller and result correction module are the core to realize control;
2、调整状态：控制环境有变化，但变化程度不大，预控制器接收的状态信息没有异常，但系统辨识模块计算得到的控制偏差也大于阈值，此时模糊模型辨识模块和模型修正模块进入工作状态，对模糊控制器的模糊模型参数进行修正，系统整体依然以模糊控制器和结果修正模块为核心实现控制；2. Adjustment state: The control environment has changed, but the change degree is not large. The state information received by the pre-controller is not abnormal, but the control deviation calculated by the system identification module is also greater than the threshold. At this time, the fuzzy model identification module and the model correction module enter In the working state, the fuzzy model parameters of the fuzzy controller are corrected, and the overall system is still controlled by the fuzzy controller and the result correction module as the core;
3、构建状态：控制环境有巨大变化，预控制器接收的状态信息异常，此时系统辨识模块不需要计算控制偏差，系统整体以模糊模型辨识模块和模型修正模块为核心，构建新的模糊模型，此时模糊控制器在收到新的模糊模型之前以原有的模糊模型进行控制，但由于系统辨识模块不计算控制偏差，因此模糊控制的过程实质上是相对稳定的，便于构建新的模糊模型。3. Construction status: There is a huge change in the control environment, and the status information received by the pre-controller is abnormal. At this time, the system identification module does not need to calculate the control deviation. The whole system uses the fuzzy model identification module and the model correction module as the core to build a new fuzzy model. , at this time, the fuzzy controller uses the original fuzzy model to control before receiving the new fuzzy model, but because the system identification module does not calculate the control deviation, the process of fuzzy control is relatively stable in essence, and it is convenient to construct a new fuzzy model Model.
一般而言，如L-K泛函等分析模型在系统辨识模块中进行计算，利用高性能芯片的计算能力暴力计算分析模型，故分析模型在根据实际控制环境而设置时，应当考虑变化因素，加入控制环境修正项。Generally speaking, analysis models such as L-K functional are calculated in the system identification module, and the analysis model is violently calculated by using the computing power of high-performance chips. Therefore, when the analysis model is set according to the actual control environment, change factors should be considered and control factors should be added. Environment modifiers.
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| 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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