Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP i...Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.展开更多
State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modele...State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.展开更多
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance....To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60025307, No. 60234010) the National 863 Project(No. 2001AA413130,2002AA412420)+1 种基金 Research Fund for the Doctoral Program of Higher Education (No. 20020003063) the National 973 Program
文摘Based on a nonlinear state predictor (NSP) and a strong tracking filter (STF), a sensor fault tolerant generic model control (FTGMC) approach for a class of nonlinear time-delay processes is proposed. First, the NSP is introduced, and it is used to extend the conventional generic model control (GMC) to nonlinear processes with large input time-delay. Then the STF is adopted to estimate process states and sensor bias, the estimated sensor bias is used to drive a fault detection logic. When a sensor fault is detected, the estimated process states by the STF will be used to construct the process output to form a 'soft sensor', which is then used by the NSP (instead of the real outputs) to provide state predictors. These procedures constitute an active fault tolerant control scheme. Finally, simulation results of a three-tank-system demonstrate the effectiveness of the proposed approach.
基金Supported by the National Natural Science Foundation of China （20476007, 20676013）.
文摘State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.
文摘To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
