改进了采样数据控制,用于使用Takagi-Sugeno模糊模型的非线性动态定位船
Minjie Zheng1, Yulai Su1, Guoquan Chen1
1Navigation College, Jimei University, Xiamen 361021, China.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
本研究介绍了一种使用Takagi-Sugeno (T-S) 模糊模型的动态定位船 (DPS) 新型采样数据控制器. 该方法通过充分利用采样信息,改善稳定性分析和控制器设计,减少保守主义.
科学领域:
- 海洋工程 海洋工程
- 控制系统理论 控制系统理论
- 模糊逻辑系统 模糊逻辑系统
背景情况:
- 动态定位系统 (DPS) 对船舶至关重要.
- 采样数据控制对于具有离散时间信息的现代控制系统至关重要.
- 塔卡吉-苏杰诺 (T-S) 模糊模型为非线性系统表示提供了一个框架.
研究的目的:
- 开发一个改进的采样数据控制器,用于动态定位船 (DPS) 使用塔卡吉-苏格诺 (T-S) 模糊模型.
- 减少DPS的稳定性分析和控制器设计中的保守主义.
- 为了确保强大的性能和解决控制系统中的模式不确定性.
主要方法:
- 构建一个改进的莱普诺夫-克拉索夫斯基函数 (LKF),并纳入新的术语.
- 应用相互凸的方法来绑定LKF的导数.
- 利用线性矩阵不等式 (LMI) 来实现稳定性条件,强大的性能和控制器设计.
主要成果:
- 拟议的LKF有效地利用整个抽样期间的抽样信息.
- 在稳定性分析和控制器设计中实现了较少的保守主义.
- 该方法在分析稳定性条件,强大的性能和模式不确定性方面表现出有效性.
结论:
- 开发的采样数据控制方法为DPS提供了更好的性能和更少的保守主义.
- 该方法有效地处理模式不确定性,并确保稳健的性能.
- 基于LMI的分析提供了一种系统的方式来设计DPS应用中的TS模糊系统的控制器.
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