强大的自适应模糊控制第二阶欧勒-拉格朗日系统的不确定性和干扰通过非线性负-想象系统理论
IEEE transactions on cybernetics
|March 1, 2024
概括
一个新的强大的自适应负-虚拟-模糊 (RANIF) 控制方案增强了不确定的系统的跟踪控制. 这种方法简化了模糊系统调整,并提高了对干扰和故障的性能.
科学领域:
- 控制理论 控制理论
- 机器人技术 机器人技术 机器人技术
- 系统动力学系统动力学
背景情况:
- 对于不确定的多输入多输出 (MIMO) 系统来说,设计强大的和适应性的控制是具有挑战性的.
- 传统的模糊控制与高度不确定的环境和复杂的调整作斗争.
- 现有的自适应模糊方法可能会受到计算复杂性的影响.
研究的目的:
- 开发一种新的,强大的,适应性的负-想象-模糊 (RANIF) 控制方案.
- 为了简化模糊控制器设计和减少计算复杂性.
- 确保全球稳定性,提高不确定的MIMO系统的跟踪性能.
主要方法:
- 整合非线性负-想象 (NI) 系统理论,自我适应的模糊控制和利亚普诺夫合成.
- 优化模糊系统参数使用自调技术与比例衍生滑动分流体.
- 使用Lyapunov,非线性NI和散散性理论的模糊规则的系统推导,具有最小的成员函数.
主要成果:
- 通过非线性NI理论证明了闭环系统的全球稳定性.
- 对不确定的MIMO二级欧勒-拉格朗日系统的模拟结果显示出卓越的性能.
- RANIF的表现优于非线性严格的NI-Fuzzy,模糊逻辑控制,模型预测控制和PID控制.
结论:
- 拟议的RANIF控制方案为干扰和故障提供了更强大的稳定性.
- 与现有方法相比,RANIF提供了优越的轨迹跟踪性能.
- 这种方法简化了调整,减少了计算复杂性,解决了
- 复杂性的爆炸.
- 问题. 问题.
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