A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e....A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.展开更多
In many applications, the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them. This decomposition is motivated by the ease and flexibility of the controller desi...In many applications, the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them. This decomposition is motivated by the ease and flexibility of the controller design for each subsystem. In this paper, a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties. The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries. The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems. The adaptive parameters are updated along iteration axis to com- pensate the interconnections among subsystems. It is shown that by using the proposed decentralized controller, the states of the subsystems can track the desired reference model states iteratively. Simulation results demonstrate that, utilizing the proposed adaptive controller, the tracking error for each subsystem converges along the iteration axis.展开更多
基金supported by Scientific Reserch Fund of SiChuan Provincial Education Department (No.07ZB013)by the Scientific ResearchFoundation of CUIT (No.CSRF200704)
文摘A new sensor fault diagnosis method based on structured kernel principal component analysis （KPCA） is proposed for nonlinear processes. By performing KPCA on subsets of variables, a set of structured residuals, i.e., scaled powers of KPCA, can be obtained in the same way as partial PCA. The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis, according to a properly designed incidence matrix. Sensor fault sensitivity and critical sensitivity are defined, based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA. The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor （CSTR） process.
基金supported by the National Grand Fundamental Research 973 Program of China(No.G2002CB312200)
文摘In many applications, the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them. This decomposition is motivated by the ease and flexibility of the controller design for each subsystem. In this paper, a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties. The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries. The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems. The adaptive parameters are updated along iteration axis to com- pensate the interconnections among subsystems. It is shown that by using the proposed decentralized controller, the states of the subsystems can track the desired reference model states iteratively. Simulation results demonstrate that, utilizing the proposed adaptive controller, the tracking error for each subsystem converges along the iteration axis.
