Stable Adaptive Neural Control of a Robot Arm

• 作者: Saeed M. Badran Salem Zerkaoui
• 来源期刊: 智能控制与自动化(英文)
• 学科: 工学
• 关键词: Adaptive Control Neural Networks MULTIVARIABLE Systems Stability ROBUSTNESS LYAPUNOV Function MANIPULATOR ROBOT
• 年，卷（期）: 2012,（2）
• 页码范围: 140-145
• 页数: 6页
• 分类号: TP1

摘要

In this paper, stable indirect adaptive control with recurrent neural networks (RNN) is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. This control scheme is applied to the manipulator robot process in order to illustrate the efficiency of the proposed method for real-world control problems.