控制理论(社会学)
非线性系统
计算机科学
趋同(经济学)
理论(学习稳定性)
李雅普诺夫函数
国家(计算机科学)
功能(生物学)
数学优化
控制(管理)
数学
算法
人工智能
量子力学
进化生物学
生物
经济增长
机器学习
物理
经济
作者
Panpan Yang,Xuyang Wang,Xingwen Chen,Shaowu Du
摘要
Abstract The fixed time event‐triggered control for high‐order nonlinear uncertain systems with time‐varying full state constraints is investigated in this paper. First, the event‐triggered control (ETC) mechanism is introduced to reduce the data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time‐varying barrier Lyapunov function (BLF) is deployed to make all the system states confined in the given time‐varying constraints. Then, the radial basis function neural networks (RBF NNs) are used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples.
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