The exponential stability of a class of switched systems containing stableand unstable subsystems with impulsive effect is analyzed by using the matrix measure concept andthe average dwell-time approach. It is shown t...The exponential stability of a class of switched systems containing stableand unstable subsystems with impulsive effect is analyzed by using the matrix measure concept andthe average dwell-time approach. It is shown that if appropriately a large amount of the averagedwell-time and the ratio of the total activation time of the subsystems with negative matrix measureto the total activation time of the subsystems with nonnegative matrix measure is chosen, theexponential stability of a desired degree is guaranteed.Using the proposed switching scheme, westudied the robust exponential stability for a class of switched systems with impulsive effect andstructure perturbations.Simulations validate the main results.展开更多
In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) t...In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neuralnetwork based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradientinformation partial deriv y/partial deriv u for optimizing the parameters of controller. Comparedwith many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzycontroller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM online. Lastly, in order to evaluate the performance of the proposed control system, the presentedcontrol system is applied to continuously stirre'd tank reactor (CSTR). Simulation comparisons,based on control effect and output error, with general fuzzy controller and feed-forward neuralfuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of RNNMrespectively using RPE algorithm and gradient learning algorithm are also compared. The results showthat the proposed control system is better for controlling uncertain and nonlinear processes.展开更多
文摘The exponential stability of a class of switched systems containing stableand unstable subsystems with impulsive effect is analyzed by using the matrix measure concept andthe average dwell-time approach. It is shown that if appropriately a large amount of the averagedwell-time and the ratio of the total activation time of the subsystems with negative matrix measureto the total activation time of the subsystems with nonnegative matrix measure is chosen, theexponential stability of a desired degree is guaranteed.Using the proposed switching scheme, westudied the robust exponential stability for a class of switched systems with impulsive effect andstructure perturbations.Simulations validate the main results.
基金The author is now working as a research fellow in the Department of Chemical & Biomolecular Engineering,Faculty of Engineering,National University of Singapore,Singapore,119260.
文摘In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neuralnetwork based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradientinformation partial deriv y/partial deriv u for optimizing the parameters of controller. Comparedwith many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzycontroller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM online. Lastly, in order to evaluate the performance of the proposed control system, the presentedcontrol system is applied to continuously stirre'd tank reactor (CSTR). Simulation comparisons,based on control effect and output error, with general fuzzy controller and feed-forward neuralfuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of RNNMrespectively using RPE algorithm and gradient learning algorithm are also compared. The results showthat the proposed control system is better for controlling uncertain and nonlinear processes.
