基于神经网络的机器人自学习控制器
Self-Learning Controller Based on Neural Networks for Robotic Manipulator
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摘要: 提出一种神经网络与PID控制相结合的机器人自学习控制器.为加快神经网络的 学习收敛性,研究了有效的优化学习算法.以两关节机器人为对象的仿真表明,该控制器使机 器人跟踪希望轨迹,其系统响应、跟踪精度和鲁棒性优于常规的控制策略.Abstract: This paper presents a new self-learning controller based on neural networks for robotic manipulator. A fast learning algorithm of neural networks is proposed to improve both speed and convergence of the learning process. Simulation results of a two-link robot show that the proposed method can give more significant performance and robustness than conventional approaches.
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Key words:
- Neural networks /
- learning algorithm /
- robot control
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