| 基于自适应模糊补偿的不确定性机器人CNF控制 |
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| 引用本文： | 蒋沅,公成龙,吕科,代冀阳.基于自适应模糊补偿的不确定性机器人CNF控制[J].振动与冲击,2020,39(8):106-111. |
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| 作者姓名： | 蒋沅 公成龙 吕科 代冀阳 |
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| 作者单位： | 1.南昌航空大学信息工程学院，南昌330063;
2.中国科学院大学工程科学学院，北京100049;
3.南昌航空大学无损检测技术教育部重点实验室，南昌330063 |
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| 基金项目： | 国家自然科学基金(61663030;61663032);江西省自然科学基金(20142BAB207021);江西省研究生教学与改革项目(JXYJG-2017-131);南昌航空大学研究生创新基金(YC2017027,2018YBXG014). |
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| 摘 要： | 为解决不确定性因素对机器人系统的影响,实现不确定性机器人系统准确跟踪参考输入的能力,研究了自适应模糊控制和组合非线性反馈(CNF)控制相结合的策略。提出了一种基于自适应模糊补偿的机器人CNF控制器。核心是将系统的不确定性利用自适应模糊控制进行在线逼近,作为CNF控制器的补偿项,充分利用两种控制方法的优势,降低不确定性因素对系统性能的影响。通过反馈线性化技术与Lyapunov理论,证明了闭环系统的收敛性;最终仿真结果证实了此方法的有效性。
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| 关 键 词： | 机器人 不确定性 自适应模糊控制 组合非线性反馈(CNF)控制 系统收敛 |
Adaptive fuzzy compensator based CNF control for uncertain robot manipulators |
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| JIANG Yuan,GONG Chenglong,L Ke,DAI Jiyang.Adaptive fuzzy compensator based CNF control for uncertain robot manipulators[J].Journal of Vibration and Shock,2020,39(8):106-111. |
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| Authors: | JIANG Yuan GONG Chenglong L Ke DAI Jiyang |
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| Affiliation: | 1.College of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;
2.School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China;
3.Key Laboratory of Nondestructive Testing Ministry of Education, Nanchang Hangkong University, Nanchang 330063, China |
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| Abstract: | In order to solve the influence of uncertain factors on a robot system and realize the ability of the uncertain robot system to track the reference input accurately, the strategy of combining adaptive fuzzy control and composite nonlinear feedback (CNF) control was studied, and a robot CNF controller based on adaptive fuzzy compensation was proposed.The core is to systematically use the adaptive fuzzy control to approach the uncertainty of the system and use it as a compensation item of the CNF controller to fully utilize the advantages of the two control methods to reduce the impact of uncertainties on the system performance.Through feedback linearization and the Lyapunov theory, the convergence of the closed-loop system was proved.The final simulation results confirm the effectiveness of the method. |
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| Keywords: | robot uncertainty adaptive fuzzy control composite nonlinear feedback(CNF) control system convergence |
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