Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distilla...Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.展开更多
State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal control for fermentation process. A novel nonlinear state estimation method for fermentation pro...State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter （CKF） to incorporate delayed measurements. The square-root version of CI（F （SCKF） algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-fine state estimation for fermentation process can be achieved by the proposed method with higher esti- mation accuracy and better stability.展开更多
This paper presents the application of the State-Dependent Riccati Equation （SDRE） method in conjunction with Kalman filter technique to design a satellite simulator control system. The performance and robustness of...This paper presents the application of the State-Dependent Riccati Equation （SDRE） method in conjunction with Kalman filter technique to design a satellite simulator control system. The performance and robustness of the SDRE controller is compared with the Linear Quadratic Regulator （LQR） controller. The Kalman filter technique is incorporated to the SDRE method to address the presence of noise in the process, measurements and incomplete state estimation. The effects of the plant non-linearities and noises （uncertainties） are considered to investigated the controller performance and robustness designed by the SDRE plus Kalman filter. A general 3-D simulator Simulink model is developed to design the SDRE controller using the states estimated by the Kalman filter. Simulations have demonstrated the validity of the proposed approach to deal with nonlinear system. The SDRE controller has presented good stability, great performance and robustness at the same time that it keeps the simplicity of having constant gain which is very important as for satellite onboard computer implementation.展开更多
文摘Composition estimation plays very important role in plant operation and control.Extended Kalman filter(EKF) is one of the most common estimators,which has been used in composition estimation of reactive batch distillation,but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium,which is difficult to initialize and tune.In this paper an inferential state estimation scheme based on adaptive neuro-fuzzy inference system(ANFIS) ,which is a model base estimator,is employed for composition estimation by using temperature measurements in multicomponent reactive batch distillation.The state estimator is supported by data from a complete dynamic model that includes component and energy balance equations accompanied with thermodynamic relations and reaction kinetics.The mathematical model is verified by pilot plant data.The simulation results show that the ANFIS estimator provides reliable and accurate estimation for component concentrations in reactive batch distillation.The estimated states form a basis for improving the performance of reactive batch distillation either through decision making of an operator or through an automatic closed-loop control scheme.
基金Supported by the National Natural Science Foundation of China(61503019)the Beijing Natural Science Foundation(4152041)Beijing Higher Education Young Elite Teacher Project(YETP0504)
文摘State estimation of biological process variables directly influences the performance of on-line monitoring and op- timal control for fermentation process. A novel nonlinear state estimation method for fermentation process is proposed using cubature Kalman filter （CKF） to incorporate delayed measurements. The square-root version of CI（F （SCKF） algorithm is given and the system with delayed measurements is described. On this basis, the sample-state augmentation method for the SCKF algorithm is provided and the implementation of the proposed algorithm is constructed. Then a nonlinear state space model for fermentation process is established and the SCKF algorithm incorporating delayed measurements based on fermentation process model is presented to implement the nonlinear state estimation. Finally, the proposed nonlinear state estimation methodology is applied to the state estimation for penicillin and industrial yeast fermentation processes. The simulation results show that the on-fine state estimation for fermentation process can be achieved by the proposed method with higher esti- mation accuracy and better stability.
文摘This paper presents the application of the State-Dependent Riccati Equation （SDRE） method in conjunction with Kalman filter technique to design a satellite simulator control system. The performance and robustness of the SDRE controller is compared with the Linear Quadratic Regulator （LQR） controller. The Kalman filter technique is incorporated to the SDRE method to address the presence of noise in the process, measurements and incomplete state estimation. The effects of the plant non-linearities and noises （uncertainties） are considered to investigated the controller performance and robustness designed by the SDRE plus Kalman filter. A general 3-D simulator Simulink model is developed to design the SDRE controller using the states estimated by the Kalman filter. Simulations have demonstrated the validity of the proposed approach to deal with nonlinear system. The SDRE controller has presented good stability, great performance and robustness at the same time that it keeps the simplicity of having constant gain which is very important as for satellite onboard computer implementation.
