用Coot优化算法调整的神经网络增强的PID控制器,用于对三环刚性机器人操纵器的强有力的轨迹跟踪
Mohamed Jasim Mohamed1, Bashra Kadhim Oleiwi1, Ahmad Taher Azar2,3,4
1Department of Control and System Engineering, University of Technology, Iraq.
Heliyon
|July 22, 2024
概括
本研究介绍了基于神经网络的机器人操纵器控制器,优化了轨迹跟踪. 神经控制器像PIPD (NN-PIPD) 控制器在模拟中表现出卓越的性能,优于其他设计.
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:
- 机器人操纵器是复杂的,非线性系统容易受到干扰和不确定性.
- 有效的控制对于实现精确的轨迹跟踪和稳定的运行至关重要.
- 现有的控制方法经常与机器人操纵器固有的复杂性作斗争.
研究的目的:
- 开发和评估一个三环刚性机器人操纵器 (3-LRRM) 的先进控制策略.
- 通过整合神经网络 (NN) 与比例,整数和导数 (PID) 控制来解决轨迹跟踪问题.
- 使用Coot优化算法 (COOA) 优化控制器参数,以提高性能和减少信号聊天.
主要方法:
- 三种不同的控制结构的设计:像PIPD这样的神经控制器 (NN-PIPD),神经网络加PID (NN+PID) 和像PID这样的Elman神经网络 (ELNN-PID).
- 使用Coot优化算法 (COOA) 调整控制器的参数,以最大限度地减少整数时方位错误 (ITSE).
- 引入了一种新的目标功能,以尽量减少调过程中的控制信号喋喋不休.
主要成果:
- 与NN+PID和ELNN-PID控制器相比,NN-PIPD控制器表现出优越的轨迹跟踪性能.
- 评估证明了控制器在拒绝干扰,处理模型不确定性和适应不同初始条件方面的有效性.
- 该NN-PIPD控制器实现了0.001777的最小ITSE,表明了最佳性能.
结论:
- 拟议的NN-PIPD控制器在机器人操纵器轨迹跟踪,干扰排斥和参数变化方面非常有效.
- 神经网络与PID控制的集成,由COOA优化,为复杂的机器人系统提供了强大的解决方案.
- 该研究强调了NN-PIPD在提高机器人操纵器控制的稳定性和稳定性方面的潜力.
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