TP381 99031711光动力学系统神经网络系统辨识及自适应控制之性能分析=Analysis of performance on the neuralnetwork adaptive control of optical dynamicsystems[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程....TP381 99031711光动力学系统神经网络系统辨识及自适应控制之性能分析=Analysis of performance on the neuralnetwork adaptive control of optical dynamicsystems[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程.—1998,6(2).展开更多
TP381 98010373混沌光学系统之快速神经网络自适应控制研究=Quick adaptive control of optical dynamic systemewith neural networks[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程.-1997,5(1).-22-27提出一种用...TP381 98010373混沌光学系统之快速神经网络自适应控制研究=Quick adaptive control of optical dynamic systemewith neural networks[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程.-1997,5(1).-22-27提出一种用于光动力学系统控制之快速神经网自适应控制技术。该技术以一前向神经网络作为光动力学系统之系统辨识器,由其与光动力学系统之输出差值对系统控制参数进行调整以达到控制目的。展开更多
A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback c...A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.展开更多
文摘TP381 99031711光动力学系统神经网络系统辨识及自适应控制之性能分析=Analysis of performance on the neuralnetwork adaptive control of optical dynamicsystems[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程.—1998,6(2).
文摘TP381 98010373混沌光学系统之快速神经网络自适应控制研究=Quick adaptive control of optical dynamic systemewith neural networks[刊,中]/杨怀江(中科院长春光机所.吉林,长春(130022))//光学精密工程.-1997,5(1).-22-27提出一种用于光动力学系统控制之快速神经网自适应控制技术。该技术以一前向神经网络作为光动力学系统之系统辨识器,由其与光动力学系统之输出差值对系统控制参数进行调整以达到控制目的。
基金Project(61433004)suppouted by the National Natural Science Foundation of China
文摘A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.
