具有输入量化和全状态约束的非严格反馈随机非线性随机系统的有限时间动态面控制
作者:
作者单位:

扬州大学信息工程学院

作者简介:

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中图分类号:

TP273

基金项目:

具有约束和多种不确定性的非线性系统自适应优化控制研究


Finite-time dynamic surface control for nonstrict-feedback nonlinear stochastic systems with input quantization and full-state constraints
Author:
Affiliation:

College of Information Engineering,Yangzhou University

Fund Project:

The National Natural Science Foundation of China

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    摘要:

    本文主要研究具有输入量化和全状态约束的随机非严格反馈非线性系统的有限时间自适应跟踪控制. 首先, 利用双曲正切函数进行非线性映射, 消除了全状态约束的限制, 将系统变换为无约束系统. 其次, 引入滞回量化器来克服量化信号中的抖动和量化误差. 第三, 为了实现有限时间控制, 提出了概率意义下半全局有限时间稳定控制方法, 加快了系统的收敛速度. 在此基础上, 采用径向基函数神经网络逼近未知非线性函数. 基于动态面控制技术和高斯函数的性质, 对变换后的非严格反馈随机系统进行自适应控制设计. 所设计的控制器能够保证闭环系统中的所有信号在概率意义下有限时间稳定. 仿真结果表明了该控制方案的有效性.

    Abstract:

    This paper mainly studies the finite-time adaptive tracking control of a class of non-strict feedback stochastic nonlinear systems with input quantization and full-state constraints. First, the hyperbolic tangent function is used for nonlinear mapping, which eliminates the constraints of the full-state constraints and transforms the system into unconstrained system. Second, a hysteresis quantizer is introduced to avoid the chattering and reduce the quantisation error in the quantized signal. Third, in order to achieve finite time control, a semi-global finite time stability criterion is proposed in the sense of probability, which speeds up the convergence speed of the system. On this basis, radial basis function neural networks are used to approximate the unknown nonlinear functions. Based on the dynamic surface control technology and the properties of the Gaussian function, adaptive control design is performed for the transformed non-strict feedback stochastic system. The designed controller can guarantee that all signals in the closed-loop system are semi-globally finite time stable in probability(SGFTSP). Simulation results show the effectiveness of the proposed control scheme.

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历史
  • 收稿日期:2021-01-05
  • 最后修改日期:2021-07-13
  • 录用日期:2021-07-19
  • 在线发布日期: 2021-08-01
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