灵活的电机机器人系统的固定时间复合神经学习控制
IEEE transactions on cybernetics
|November 17, 2023
概括
这项研究引入了对不确定灵活的远程机器人系统的固定时间控制,提高了同步精度和对干扰的稳定性. 这种新的方法确保了稳定性,并避免了现实应用中的复杂性问题.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 神经网络的神经网络的神经网络
背景情况:
- 灵活的远程机器人系统面临来自未知的合,不确定性和干扰的挑战.
- 现有的控制方法往往难以应对复杂性和暂时性能的问题.
研究的目的:
- 为不确定的灵活的远程机器人系统制定固定时间同步控制策略.
- 为了提高稳定性,精度和短暂响应,同时管理系统的不确定性和干扰.
主要方法:
- 复合适应神经网络 (CANNs) 用于估计不确定性和干扰.
- 固定时间阻抗控制策略,以实现精确的同步.
- 创新的固定时间命令波器和补偿信号,以避免复杂性爆炸.
- 利亚普诺夫稳定定理用于理论分析.
主要成果:
- 成功估计一次性系统不确定性和外部干扰.
- 实现了快速过渡,强大,高精度的位置/力同步.
- 避免了与传统后退方法相关的复杂性爆炸.
- 为控制器参数和固定时间稳定性提供了理论条件.
- 在复杂的传输时间延迟下证明了绝对的稳定性.
结论:
- 拟议的固定时间控制算法有效地解决了灵活的远程机器人系统中的不确定性和干扰.
- 该方法确保了高性能同步,并提高了稳定性和稳定性.
- 模拟结果验证了该算法的实际适用性,用于双链灵活的远程机器人系统.
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