控制理论(社会学)
非线性系统
人工神经网络
自适应控制
控制器(灌溉)
李雅普诺夫函数
计算机科学
趋同(经济学)
Lyapunov稳定性
线性系统
有界函数
线性模型
跟踪误差
数学
人工智能
控制(管理)
数学分析
物理
量子力学
机器学习
农学
经济
生物
经济增长
作者
Mohammad Abu Jami‘in,Imam Sutrisno,Jinglu Hu,Norman Mariun,Mohammad Hamiruce Marhaban
标识
DOI:10.1109/icarcv.2014.7064314
摘要
A quasi-ARX (quasi-linear ARX) neural network (QARXNN) model is able to demonstrate its ability for identification and prediction highly nonlinear system. The model is simplified by a linear correlation between the input vector and its nonlinear coefficients. The coefficients are used to parameterize the input vector performed by an embedded system called as state dependent parameter estimation (SDPE), which is executed by multi layer parceptron neural network (MLPNN). SDPE consists of the linear and nonlinear parts. The controller law is derived via SDPE of the linear and nonlinear parts through switching mechanism. The dynamic tracking controller error is derived then the stability analysis of the closed-loop controller is performed based Lyapunov theorem. Linear based adaptive robust control and nonlinear based adaptive robust control is performed with the switching of the linear and nonlinear parts parameters based Lyapunov theorem to guarantee bounded and convergence error.
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