NEURAL-NETWORK CONTROL FOR A CLOSED-LOOP SYSTEM USING FEEDBACK-ERROR-LEARNING
NEURAL-NETWORK CONTROL FOR A CLOSED-LOOP SYSTEM USING FEEDBACK-ERROR-LEARNING
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DOI：
10.1016/s0893-6080(09)80004-x
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发表时间：
1993-01-01
期刊：
影响因子：
7.8
通讯作者：
KAWATO, M
中科院分区：
文献类型：
--
作者：
GOMI, H;KAWATO, M
本文提出了一种新的学习方案，利用反馈误差学习的神经网络模型应用于自适应非线性反馈控制。提出了反馈误差学习作为用于形成前馈控制器的学习方法，该前馈控制器使用反馈控制器的输出作为用于训练神经网络模型的误差。使用新的非线性反馈控制方案，学习后的实际响应对应于由作为常规反馈控制器实现的逆参考模型定义的期望响应。在这方面，这些方法类似于应用于线性或线性化系统的模型参考自适应控制（MRAC）。结果表明，学习阻抗控制时，提出的计划是在笛卡尔空间中使用。我们将这些学习计划应用于倒立摆和2连杆机械手的结果。我们还讨论了这些学习计划中采用的神经网络模型的收敛特性，通过应用李雅普诺夫方法与描述系统动力学的随机微分方程相关联的平均方程。
This paper presents new learning schemes using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. Feedback-error-learning was proposed as a learning method for forming a feedforward controller that uses the output of a feedback controller as the error for training a neural network model. Using new schemes for nonlinear feedback control, the actual responses after learning correspond to the desired responses which are defined by an inverse reference model implemented as a conventional feedback controller In this respect, these methods are similar to Model Reference Adaptive Control (MRAC) applied to linear or linearized systems. It is shown that learning impedance control is derived when one proposed scheme is used in Cartesian space. We show the results of applying these learning schemes to an inverted pendulum and a 2-link manipulator We also discuss the convergence properties of the neural network models employed in these learning schemes by applying the Lyapunov method to the averaged equations associated with the stochastic differential equations which describe the system dynamics.