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Development of STEP AP224 Extractor for Interfacing Feature Based CAPP to STEP-NC (AP238) 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 655-670
作者:  Deepanshu Srivastava;  Venkateswara Rao Komma
浏览  |  Adobe PDF(3913Kb)  |  收藏  |  浏览/下载：108/38  |  提交时间：2021/02/22
Reduced-order observer  discrete-time systems  Lipschitz systems  H∞  ARDUINO MEGA 2560 device.  
New LMI Conditions for Reduced-order Observer of Lipschitz Discrete-time Systems: Numerical and Experimental Results 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 644-654
作者:  Noussaiba Gasmi;  Assem Thabet;  Mohamed Aoun
Adobe PDF(1366Kb)  |  收藏  |  浏览/下载：87/25  |  提交时间：2021/02/22
Reduced-order observer  discrete-time systems  Lipschitz systems  H∞  ARDUINO MEGA 2560 device.  
Stabilization for a Class of Discrete-time Switched Large-scale Systems with Parameter Uncertainties 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 543-552
作者:  Chang-Chun Sun
Adobe PDF(1611Kb)  |  收藏  |  浏览/下载：95/41  |  提交时间：2021/02/22
Switched large-scale systems  discrete-time  state feedback  decentralized control  switching law.  
Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 2, 页码: 213-225
作者:  Imen Zaidi;  Mohamed Chtourou;  Mohamed Djemel
Adobe PDF(10306Kb)  |  收藏  |  浏览/下载：130/32  |  提交时间：2021/02/22
Discrete time uncertain nonlinear systems  neural modelling  sliding mode  backpropagation (BP) algorithm  robust neural control.  
Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 1, 页码: 295-305
作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip
收藏  |  浏览/下载：302/0  |  提交时间：2019/07/12
Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning