自组织型-2 模糊双循环反复神经网络用于不确定的非线性系统控制.
概括
一个新的双循环循环神经网络 (DLRNN) 增强了机器人在不确定的环境中的控制. 该系统为非线性动态机器人操纵器提供了卓越的稳定性和稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 机器人系统在日常生活中至关重要,但在非结构化环境中面临控制挑战.
- 现有的控制方法与机器人应用固有的非线性动态和不确定性作斗争.
研究的目的:
- 引入一个双循环循环神经网络 (DLRNN),与2型模糊系统集成,并为先进的非线性机器人控制提供自我组织.
- 增强机器人控制系统在不确定的条件下的动态映射能力和适应性.
主要方法:
- 开发一个具有独特双循环结构的DLRNN,以改善动态映射.
- 集成Type-2模糊系统以提高不确定的环境中的性能.
- 在DLRNN中实施适应层调整的自我组织机制.
- 将DLRNN与滑动模式控制 (SMC) 结合起来,以确保理论和实证的稳定性.
主要成果:
- 拟议的基于DLRNN的SMC系统在与现有控制方法相比,表现优越.
- 该系统在应用于三关节机器人操纵器时被证明是有效和稳健的.
- 实验结果验证了在处理外部干扰时增强的动态映射和适应能力.
结论:
- 拟议的DLRNN具有Type-2模糊逻辑和自我组织,为非线性动态机器人控制提供了显著的进步.
- 集成系统为在具有挑战性和不确定的环境中运行的机器人应用程序提供了稳定,有效和强大的解决方案.
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