Learning control for a closed loop system using feedback-error-learning
Learning control for a closed loop system using feedback-error-learning
复制标题
使用反馈误差学习的闭环系统的学习控制
DOI：
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发表时间：
1990
期刊：
影响因子：
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通讯作者：
M. Kawato
中科院分区：
文献类型：
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作者：
H. Gomi;M. Kawato
提出了一种用于自适应非线性反馈控制的神经网络模型的反馈误差学习方法。在神经网络通过学习完全或部分补偿被控对象的非线性后，被控对象的响应遵循常规反馈控制器中的期望设定。这种学习方案不需要预先知道被控对象的非线性。使用所提出的方法，学习后的实际响应对应于期望的响应。当需要笛卡尔空间中的期望响应时，导出了学习阻抗控制。利用平均方程和李雅普诺夫方法证明了神经网络的收敛性。这种学习方法的仿真结果。所提出的方案可用于许多类型的控制对象，如化工厂，机器和机器人。<<ETX>>
The authors propose a learning scheme using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. After the neural network compensates perfectly or partially for the nonlinearity of the controlled object through learning, the response of the controlled object follows the desired set in the conventional feedback controller. This learning scheme does not require the knowledge of the nonlinearity of a controlled object in advance. Using the proposed approach, the actual responses after learning correspond to desired responses. When the desired response in Cartesian space is required, learning impedance control is derived. The convergence properties of the neural networks are provided by the averaged equation and Lyapunov method. Simulation results on this learning approach are presented. The proposed scheme can be used for many kinds of controlled objects, such as chemical plants, machines, and robots.<<ETX>>