Distributed containment control for first-order and second-order multi-agent systems with delays and position-constraints [0.03%]
具有时延和位置约束的一阶及二阶多智能体系统的分布式包含控制
Wei Cui,Ning Gao,Yikang Yang
Wei Cui
This work addresses the discrete-time containment control problem for first-order and second-order systems, incorporating position constraints, nonuniform time delays, and switching topologies. Projection operators are introduced to maintai...
Fault diagnosis and tolerant strategy for MIMO system based on ν-gap metric [0.03%]
基于ν-间隙度量的MIMO系统的故障诊断与容错策略研究
Shufeng Zhang,Changan Liu,Yuntao Shi et al.
Shufeng Zhang et al.
The coupled relationship between inputs and outputs in multiple-input multiple-output (MIMO) systems, as well as the multiplicative uncertainties caused by multiplicative faults, increases the complexity of fault diagnosis (FD) and fault-to...
Adaptive robust motion control for hydraulic load sensitive systems considering displacement dynamic compensation [0.03%]
基于位移动态补偿的液压负载敏感系统自适应鲁棒运动控制
Zhongyi Qiu,Xiaochao Liu,Zhenyu Wang et al.
Zhongyi Qiu et al.
Hydraulic load-sensitive systems (HLSS) are widely used for high power density and energy efficiency. This study introduces an adaptive, energy-efficient HLSS with a valve-controlled variable motor. The system faces challenges from non-line...
Online estimation method for extreme learning machine with kernels based on the multi-innovation theory and intelligent optimization strategy [0.03%]
基于多创新理论和智能优化策略的核泛化极限学习机在线估计方法
Yanjiao Wang,Yiting Liu,Weidi Li et al.
Yanjiao Wang et al.
In order to effectively model data online, a learning model must not only have the high adaptability of dynamic data but also keep the low complexity to meet the online computing requirements. In this paper, a novel multi-innovation online ...
Adaptive RISE-based tracking control of uncertain nonlinear systems: A FAS approach [0.03%]
基于自适应RISE的不确定非线性系统的跟踪控制：一种FAS方法
Zhijun Chen,Guangren Duan
Zhijun Chen
A fully actuated system (FAS) approach integrated with adaptive robust integral of the sign of the error (ARISE) feedback control strategy is proposed for multi-input multi-output nonlinear systems in the presence of both external disturban...
Random Fourier features based nonlinear recurrent kernel normalized LMS algorithm with multiple feedbacks [0.03%]
基于随机傅立叶特征的非线性多回路核归一化LMS算法
Ji Zhao,Jiaming Liu,Qiang Li et al.
Ji Zhao et al.
The performance of kernel adaptive filtering algorithms (KAFs) with nonlinear recurrent structures surpasses traditional KAFs, attributed to the nonlinear contribution of feedback. Nevertheless, the existing nonlinear recurrent KAFs primari...
Adaptive quantized finite-time fault-tolerant control for uncertain multi-input multi-output systems and its application [0.03%]
不确定多输入多输出系统的自适应量化有限时间容错控制及其应用研究
Yue Sun,Ming Chen,Yu-Lin Gai et al.
Yue Sun et al.
The article proposes a novel state-feedback control method for a multiple-input multiple-output (MIMO) nonlinear system with actuator faults and input quantization. The innovation of the design approach lies in the utilization of fuzzy logi...
Wenjing Wang,Wei Wang,Juanjuan Xu
Wenjing Wang
This paper considers the exact controllability of Itô stochastic systems with input delay. In particular, one delay-free controller and one delayed controller are involved in the systems which complicates the study due to the inconsistency...
A new formal approach to safe response time upper bound evaluation for distributed control systems using dioid algebra [0.03%]
利用 dioide 代数评估分布式控制系统的安全响应时间上限的一种新形式化方法
Louis Bal Dit Sollier,Alain Ourghanlian,Saïd Amari
Louis Bal Dit Sollier
This paper presents an approach to computing a safe upper bound of the response time of Distributed Control System (DCS). From a graphical model of live autonomous Timed Event Graphs (TEGs) with asynchronous links and weights, we draw algeb...
A hyperparameter optimization-assisted deep learning method towards thermal error modeling of spindles [0.03%]
基于超参数优化的深锥域学习 spindle热误差模型方法
Shicun Ao,Sitong Xiang,Jianguo Yang
Shicun Ao
Spindle thermal errors significantly influence the machining accuracy of machine tools, necessitating precise modeling. While deep learning methods are commonly used for this purpose, their generalization ability and performance largely dep...