【摘要】：在控制領(lǐng)域中,比例-積分-微分(PID)控制器是使用最多的一種控制器。因其簡單結(jié)構(gòu)、魯棒性強(qiáng)以及便于實(shí)現(xiàn)等優(yōu)點(diǎn),所以在工業(yè)過程中被廣泛使用。但是,由于工業(yè)過程變得越來越復(fù)雜,導(dǎo)致生產(chǎn)過程中不可避免的出現(xiàn)了時(shí)滯、非線性以及不確定性等問題,使得PID控制器越來越難滿足需要的控制性能。預(yù)測(cè)函數(shù)控制(PFC)作為預(yù)測(cè)控制中被廣泛使用的算法,所以被廣泛應(yīng)用于石油化工過程中。主要是PFC對(duì)模型的精度要求不高,并且具有較高的魯棒性和跟蹤性能。模糊控制是在智能控制算法中經(jīng)常被使用到的一種算法,目前已經(jīng)在工業(yè)過程控制中被廣泛使用。這是因?yàn)槠洳灰蕾囉跀?shù)學(xué)模型,僅僅通過相關(guān)經(jīng)驗(yàn)以及數(shù)據(jù)就能夠很好的控制被控對(duì)象,所以在控制領(lǐng)域具有越來越重要的地位。如果能夠?qū)ID控制與模糊控制和PFC這兩種算法的優(yōu)點(diǎn)進(jìn)行結(jié)合,將會(huì)對(duì)工業(yè)上的生產(chǎn)效率進(jìn)行改善。本文通過總結(jié)前人優(yōu)秀的成果,然后做了如下兩方面的研究工作:一方面,通過將模糊理論與神經(jīng)網(wǎng)絡(luò)的相關(guān)性能進(jìn)行結(jié)合,在此基礎(chǔ)上提出了模糊網(wǎng)絡(luò)PID控制器的一種設(shè)計(jì)方法。利用神經(jīng)網(wǎng)絡(luò)本身具有的自學(xué)能力與模糊推理能力相結(jié)合來對(duì)PID參數(shù)進(jìn)行調(diào)整,從而提高了PID控制的自適應(yīng)能力。最后,通過仿真來對(duì)模糊網(wǎng)絡(luò)PID控制性能進(jìn)行了驗(yàn)證。另一方面,通過結(jié)合預(yù)測(cè)控制、模糊控制和PID控制各自的優(yōu)點(diǎn),提出一種預(yù)測(cè)模糊PID控制器的設(shè)計(jì)方法。該方法是通過模糊控制達(dá)到自適應(yīng)調(diào)整,并引入預(yù)測(cè)控制的預(yù)測(cè)模型來完成提前預(yù)測(cè)。從而在保證了該控制器具有了預(yù)測(cè)控制的預(yù)測(cè)能力外,還同時(shí)具有模糊控制的推理能力。最后將該控制方法應(yīng)用到工業(yè)中的焦化爐被控對(duì)象上,并結(jié)合仿真來對(duì)該設(shè)計(jì)方法的有效性進(jìn)行了驗(yàn)證。
[Abstract]:In the field of control, the proportional-integral-differential (PID) controller is one of the most widely used controllers. Because of its simple structure, strong robustness and easy to implement, it is widely used in industrial processes. However, as the industrial process becomes more and more complex, it is inevitable that there are some problems such as delay, nonlinearity and uncertainty in the production process, which makes the PID controller more and more difficult to meet the required control performance. Predictive function control (PFC) is widely used as an algorithm in predictive control, so it is widely used in petrochemical process. The main problem is that PFC has low precision and high robustness and tracking performance. Fuzzy control is often used in intelligent control algorithms and has been widely used in industrial process control. This is because it does not rely on the mathematical model, only through the relevant experience and data can control the controlled object very well, so it has more and more important position in the control field. If the advantages of PID control, fuzzy control and PFC can be combined, the industrial production efficiency will be improved. This paper summarizes the outstanding achievements of the predecessors, and then does the following two aspects of research: on the one hand, by combining the fuzzy theory with the correlation of neural networks, On this basis, a design method of fuzzy network PID controller is proposed. The self-learning ability of neural network and fuzzy reasoning ability are combined to adjust the PID parameters, thus improving the adaptive ability of PID control. Finally, the performance of fuzzy network PID control is verified by simulation. On the other hand, combining the advantages of predictive control, fuzzy control and PID control, a design method of predictive fuzzy PID controller is proposed. In this method, adaptive adjustment is achieved by fuzzy control, and the predictive model of predictive control is introduced to complete the prediction in advance. Thus, the controller has the predictive ability of predictive control and the reasoning ability of fuzzy control. Finally, the control method is applied to the controlled object of coking furnace in industry, and the validity of the design method is verified by simulation.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP273

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李佛W，

本文編號(hào)：2388665