Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
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DOI：
10.1109/tnnls.2017.2665581
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发表时间：
2018-04-01
影响因子：
10.4
通讯作者：
Dong, Yiting
中科院分区：
文献类型：
--
作者：
He, Wei;Dong, Yiting
针对未知系统动力学、状态约束的影响以及机器人接触的不确定柔顺环境，研究了基于阻抗学习的受限机器人自适应模糊神经网络控制。提出了一种模糊神经网络学习算法来辨识不确定对象模型。模糊神经网络的突出特点是不需要获得关于不确定性的先验知识和足够多的观测数据。此外，还引入了阻抗学习来处理机器人与环境之间的相互作用，使机器人跟踪由阻抗学习产生的期望目的地。采用势垒李亚普诺夫函数来处理状态约束的影响。利用李亚普诺夫稳定性理论实现了闭环系统的稳定性，并保证了在状态约束和不确定性条件下的跟踪性能。通过仿真研究，验证了该方案的有效性。
This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov's stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.