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| 三级倒立摆的自适应神经模糊控制 | |
| 引用本文： | 高军伟,蔡国强,纪志坚,秦勇,贾利民.三级倒立摆的自适应神经模糊控制[J].控制理论与应用,2010,27(2):278-282. |
| 作者姓名： | 高军伟 蔡国强 纪志坚 秦勇 贾利民 |
| 作者单位： | 1. 青岛大学自动化工程学院,山东,青岛,266071;北京交通大学轨道交通控制与安全国家重点实验室,北京,100044 2. 北京交通大学轨道交通控制与安全国家重点实验室,北京,100044 3. 青岛大学自动化工程学院,山东,青岛,266071 |
| 基金项目： | supported in part by the National Natural Science Foundation of China(60604032, 60674001); in part by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University(RCS2008ZZ04,SKL2007K006); Shandong Province Domestic Visitor Foundation; in part by the the National 863 High Technology Plan of China(2007AA11Z247). |
| 摘 要： | 在三级倒立摆(TIP)系统中, 应用神经网络与模糊控制相结合的自适应神经模糊推理系统(adaptive neuralfuzzy inference system), 根据样本数据调整隶属函数和控制规则参数, 使得训练后ANFIS控制器很好地模拟期望的输入输出数据. 仿真结果表明所设计的ANFIS控制器对三级倒立摆系统的稳定控制是可行的. 与LQR控制相比, 基于ANFIS控制的倒立摆系统具有良好的动态性能和抗干扰性能. |
| 关 键 词： | 三级倒立摆 自适应神经模糊推理系统 状态合成 LQR |
| 收稿时间： | 2009/6/30 0:00:00 |
| 修稿时间： | 2009/9/10 0:00:00 |
Adaptive neural-fuzzy control of triple inverted pendulum |
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| GAO Jun-wei,CAI Guo-qiang,JI Zhi-jian,QIN Yong and JIA Li-min.Adaptive neural-fuzzy control of triple inverted pendulum[J].Control Theory & Applications,2010,27(2):278-282. | |
| Authors: | GAO Jun-wei CAI Guo-qiang JI Zhi-jian QIN Yong and JIA Li-min |
| Affiliation: | College of Automation Engineering, Qingdao University; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University,College of Automation Engineering, Qingdao University,State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University,State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University |
| Abstract: | In the triple inverted pendulum(TIP) system, adaptive neural-fuzzy inference system(ANFIS) approach is atilized to combine fuzzy logic with Neural-Network, according to the input/output data, so that ANFIS automatically adjusts fuzzy rules and membership functions based on state synthesis to fit sampling data. The simulation results show that the designed ANFIS controller is feasible. Compared with LQR control, triple inverted pendulum based on ANFIS control has better dynamics performance and anti-interference capability. |
| Keywords: | triple inverted pendulum adaptive neural-fuzzy inference state synthesis LQR |
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