利用指令滤波策略实现输出受限高阶系统的固定时间自适应神经网络跟踪控制

IF 3.9 4区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Lian Chen, Junzhong Tang, Song Ling
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引用次数: 0

摘要

摘要 本文在添加功率积分器技术的基础上,针对一类高阶系统提出了一种固定时间自适应神经指令滤波控制器。与现有研究不同,本文提出的控制器具有以下显著优势:(i) 将固定时间控制框架扩展到高阶系统的跟踪控制问题。(ii) 误差补偿机制消除了动态控制器产生的滤波误差。(iii) 借助自适应神经网络,放宽了对未知函数增长的假设。(iv) 更通用的系统:所开发的控制器可适用于不确定动态、未知增益函数和非对称约束的高阶系统。稳定性分析表明,所有状态都是半全局均匀终界的,跟踪误差的收敛速率与初始条件无关。最后,仿真结果验证了所开发控制方案的优势和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fixed-time adaptive neural network tracking control for output-constrained high-order systems using command filtered strategy

This article proposes a fixed-time adaptive neural command filtered controller for a category of high-order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: (i) a fixed-time control framework is extended to the tracking control problem of high-order systems. (ii) The error compensation mechanism eliminates filter errors that arise from dynamic controllers. (iii) Growth assumptions about unknown functions are relaxed with the help of adaptive neural networks. (iv) More general systems: the developed controller can apply to high-order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi-globally uniformly ultimately bounded, and the convergence rate of tracking error is independent of initial conditions. Finally, simulation results validate the advantages and efficacy of the developed control scheme.

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来源期刊
CiteScore
5.30
自引率
16.10%
发文量
163
审稿时长
5 months
期刊介绍: The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.
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