基于斑马优化算法的双链刚性机器人操纵器的神经网络PID/FOPID操作的混合控制器.
Mohamed Jasim Mohamed1, Bashra Kadhim Oleiwi1, Ahmad Taher Azar2,3,4
1Control and Systems Engineering Department, University of Technology-Iraq, Baghdad, Iraq.
Frontiers in robotics and AI
|July 1, 2024
概括
这项研究介绍了六种基于神经网络的机器人操纵器控制器,增强了轨迹跟踪. 该NN+FOPID控制器表现出卓越的性能和稳定性,可以应对干扰和不确定性.
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
- 人工智能的人工智能
背景情况:
- 机器人操纵者面临来自外部干扰,参数不确定性和复杂非线性动态的挑战.
- 控制多输入,多输出 (MIMO) 系统,如机器人操纵器,需要先进的控制策略.
- 现有的控制器可能会与机器人系统的合性和非线性性质作斗争.
研究的目的:
- 提出和评估六个新的控制结构,用于一个专注于轨迹跟踪的2链刚性机器人操纵器 (2-LRRM).
- 研究将神经网络 (NN) 与比例积分导数 (PID) 和分数顺序 PID (FOPID) 控制器相结合的有效性.
- 开发一个强大的控制器,尽量减少积分时平方误差 (ITSE) 和控制信号聊天.
主要方法:
- 设计了六个控制结构:设定点加权PID (W-PID),W-FOPID,反复的神经网络 (RNN) 类PID (RNNPID),RNN-类FOPID (RNN-FOPID),NN+PID和NN+FOPID.
- 斑马优化算法 (ZOA) 用于优化控制器参数,最大限度地减少ITSE.
- 引入了一种新的目标功能,以减少调过程中控制信号的喋喋不休.
- 通过不同的初始条件,干扰和模型不确定性进行了比较稳定性分析.
主要成果:
- 在NN+FOPID控制器中,NN+FOPID控制器在评估的控制器中表现出最佳的轨迹跟踪性能.
- 该NN+FOPID控制器实现了最小积分时间平方误差 (ITSE).
- 这个控制器在初始状态的变化,外部干扰和参数不确定性方面表现出卓越的稳定性.
结论:
- 拟议的NN+FOPID控制器为机器人操纵器的轨迹跟踪提供了显著的改进.
- 将神经网络与分数顺序PID控制器相结合,在复杂的机器人系统中提供了增强的稳定性和性能.
- 开发的控制策略有效地解决了机器人操纵器控制中非线性动态,干扰和不确定性所带来的挑战.
相关概念视频
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01:22Controller Configurations
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