倒立擺系統是一種典型的非線性、多變量、不穩定系統，目前，對于這種復雜對象的控制問題在控制領域具有十分重要的研究價值。針對此種非線性系統的控制問題，提出一種智能控制方法來解決這個問題。通過應用神經網絡控制和模糊控制相結合的方式，集合二者的優點，提出一種將BP算法與最小二乘算法相結合的算法，對Takagi-Sugeno模糊推理系統中的參數進行優化修正，設計一種自適應神經網絡的模糊推理系統來控制倒立擺，實驗結果證明該理論是準確可行的，與LQR實時控制相比響應速度快、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.