This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density fun...This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved.展开更多
A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore...A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore converts the PDF-shaping into the control of B-spline weights vector. The Diophantine equation is then introduced to formulate the predictive PDF model, based on which a moving-horizon control algorithm is developed so as to realize the predictive PDF tracking performance.展开更多
A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where ...A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.展开更多
The task of robust fault detection and diagnosis of stochastic distribution control(SDC)systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of...The task of robust fault detection and diagnosis of stochastic distribution control(SDC)systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system.Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input,in this paper,a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties.When certain conditions are satisfied,the weight vector of the rational square-root B-spline model proves to be bounded.Conver-gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase.Computer simulations are given to demon-strate the effectiveness of the proposed algorithm.展开更多
文摘This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved.
基金Supported by National Basic Research Program of China （973 Program） （2009CB320600）, National Natural Science Foundation of China （60828007, 60534010, 60821063）, the Leverhulme Trust （F/00. 120/BC） in the United Kingdom, and the 111 Project （B08015）
基金Supported by National Natural Science Foundation of P. R. China (60128303)
文摘A predictive control strategy is proposed for the shaping of the output probability density function (PDF) of linear stochastic systems. The B-spline neural network is used to set up the output PDF model and therefore converts the PDF-shaping into the control of B-spline weights vector. The Diophantine equation is then introduced to formulate the predictive PDF model, based on which a moving-horizon control algorithm is developed so as to realize the predictive PDF tracking performance.
基金the Outstanding Oversea Award of the Chinese Academy of Sciences (No. 2004-1-4)the Natural Science Foundationof China (No. 60534010)
文摘A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.
基金Supported by the National Natural Science Foundation of China(Grant No.60534010)the Outstanding Overseas Chinese Scholars Fund of CAS(Grant No.2004-1-4)
文摘The task of robust fault detection and diagnosis of stochastic distribution control(SDC)systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system.Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input,in this paper,a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties.When certain conditions are satisfied,the weight vector of the rational square-root B-spline model proves to be bounded.Conver-gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase.Computer simulations are given to demon-strate the effectiveness of the proposed algorithm.
