基于神经网络的自适应固定时间控制,用于具有输入量化和输出约束的2DOF直升机系统
概括
本研究介绍了非线性直升机的神经网络控制,确保在固定的时间内稳定,尽管输入量化和输出约束. 适应性技术和屏障功能保证了系统的性能和安全.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 非线性系统动态 非线性系统动态
- 在工程领域的人工智能.
背景情况:
- 由于其固有的复杂性,非线性直升机系统存在重大控制挑战.
- 输入量化和输出约束可以降低控制性能和系统稳定性.
- 在这种情况下,现有的控制方法可能难以保证固定时间的趋同.
研究的目的:
- 为2DOF非线性直升机开发一种新的适应性固定时间控制策略.
- 为应对输入量化和输出约束所带来的挑战.
- 为了确保系统信号在预先确定的固定时间内保持局限.
主要方法:
- 使用歇斯底里量化仪和适应变量来管理输入量化和相关错误.
- 辐射基函数神经网络 (NN) 估计的系统不确定性.
- 一个对数障碍利亚普诺夫函数 (BLF) 强制执行输出约束.
- 使用了利亚普诺夫稳定性分析和固定时间稳定性标准.
主要成果:
- 提出的基于NN的自适应控制方法有效地减轻了量子化引起的聊.
- 系统的不确定性被辐射基函数NN准确地近似.
- 对数障碍力普诺夫函数成功阻止了输出约束违规.
- 封闭循环系统的信号被严格证明是固定时间内的.
结论:
- 开发的控制策略确保了2-DOF非线性直升机系统的稳定和稳定的运行.
- 该方法在处理输入量化和输出约束方面表现出有效性.
- 数字模拟和实验验证证证了拟议方法的实际可行性.
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