倒立擺系統(tǒng)是一種典型的非線性、多變量、不穩(wěn)定系統(tǒng)，目前，對于這種復(fù)雜對象的控制問題在控制領(lǐng)域具有十分重要的研究價值。針對此種非線性系統(tǒng)的控制問題，提出一種智能控制方法來解決這個問題。通過應(yīng)用神經(jīng)網(wǎng)絡(luò)控制和模糊控制相結(jié)合的方式，集合二者的優(yōu)點，提出一種將BP算法與最小二乘算法相結(jié)合的算法，對Takagi-Sugeno模糊推理系統(tǒng)中的參數(shù)進(jìn)行優(yōu)化修正，設(shè)計一種自適應(yīng)神經(jīng)網(wǎng)絡(luò)的模糊推理系統(tǒng)來控制倒立擺，實驗結(jié)果證明該理論是準(zhǔn)確可行的，與LQR實時控制相比響應(yīng)速度快、精度高。

Abstract:

?The inverted pendulum system is characterized as a typical nonlinear， multi-variable， unstable system. At present， the research on the control of complex objects has great value in control area. This article is based on the control of the nonlinear system and put forward one intelligent method to solve this problem. This method combined neural network control and fuzzy control reflects the advantages of them. A mixed arithmetic of BP and LSE arithmetic are used， in order to optimize and amend the parameters of Takagi-Sugeno fuzzy model. A sort of self-adaptive neural-fuzzy inference system is put forward for control of inverted pendulum. The result of experiment shows this theory is feasible and effective. The response speed is faster than LQR control. And the precision is higher than it， too.