固定时间自适应神经网络对不确定的非线性系统进行补偿控制
Jiahua Ma1, Zhikai Yao2, Wenxiang Deng1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, China.
概括
本研究引入了一种新的固定时间自适应神经网络控制方法,以提高具有不确定性的非线性系统的性能. 该方法确保了固定的时间稳定性,克服了复杂系统中的控制限制.
科学领域:
- 控制系统工程 控制系统工程
- 非线性动力学是一种非线性动力学.
- 人工智能在控制中
背景情况:
- 非线性系统中的不确定性阻碍了控制性能.
- 高级非线性系统带来了重要的控制挑战.
- 现有的方法可能会受到保守主义或差异性爆炸的影响.
研究的目的:
- 开发一个固定时间的自适应神经网络补偿控制方法.
- 为了解决不确定的非线性和参数不确定性.
- 提高控制性能,确保固定时间稳定性.
主要方法:
- 为不确定的非线性设计了一个固定时间自适应神经网络 (FTANN).
- 为参数不确定性开发了一个新的固定时间自适应定律.
- 在动态表面控制 (DSC) 框架内,集成FTANN和自适应法与增强自适应的固定时间波器.
主要成果:
- 拟议的控制器保证了所有系统状态的固定时间稳定性,这是由Lyapunov分析证明的.
- 该方法解决了一些控制设计中固有的"微分爆炸"问题.
- 通过降低强大的反收益,减少了控制器保守主义.
结论:
- 新的固定时间自适应神经网络控制方法有效地管理高阶非线性系统中的不确定性.
- 综合方法确保在固定的时间内确保系统稳定性.
- 模拟和实验结果验证了控制器的卓越性能.
相关概念视频
Control Systems
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At the heart...
At the heart...
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Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Consider the example of control of motor torque. Initially, a positive...
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