This paper deals with the problem of active disturbance rejection control(ADRC)design for a class of uncertain nonlinear systems with sporadic measurements.A novel extended state observer(ESO)is designed in a cascade ...This paper deals with the problem of active disturbance rejection control(ADRC)design for a class of uncertain nonlinear systems with sporadic measurements.A novel extended state observer(ESO)is designed in a cascade form consisting of a continuous time estimator,a continuous observation error predictor,and a reset compensator.The proposed ESO estimates not only the system state but also the total uncertainty,which may include the effects of the external perturbation,the parametric uncertainty,and the unknown nonlinear dynamics.Such a reset compensator,whose state is reset to zero whenever a new measurement arrives,is used to calibrate the predictor.Due to the cascade structure,the resulting error dynamics system is presented in a non-hybrid form,and accordingly,analyzed in a general sampled-data system framework.Based on the output of the ESO,a continuous ADRC law is then developed.The convergence of the resulting closed-loop system is proved under given conditions.Two numerical simulations demonstrate the effectiveness of the proposed control method.展开更多
The classical probabilistic reliability theory and fuzzy reliability theory cannot directly measure the uncertainty of structural reliability with uncertain variables, i.e., subjective random and fuzzy variables. In o...The classical probabilistic reliability theory and fuzzy reliability theory cannot directly measure the uncertainty of structural reliability with uncertain variables, i.e., subjective random and fuzzy variables. In order to simultaneously satisfy the duality of randomness and subadditivity of fuzziness in the reliability problem, a new quantification method for the reliability of structures is presented based on uncertainty theory, and an uncertainty-theory-based perspective of classical Cornell reliability index is explored. In this paper, by introducing the uncertainty theory, we adopt the uncertain measure to quantify the reliability of structures for the subjective probability or fuzzy variables, instead of probabilistic and possibilistic measures. We utilize uncertain variables to uniformly represent the subjective random and fuzzy parameters, based on which we derive solutions to analyze the uncertainty reliability of structures with uncertainty distributions. Moreover, we propose the Cornell uncertainty reliability index based on the uncertain expected value and variance.Experimental results on three numerical applications demonstrate the validity of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(61833016,61873295).
文摘This paper deals with the problem of active disturbance rejection control(ADRC)design for a class of uncertain nonlinear systems with sporadic measurements.A novel extended state observer(ESO)is designed in a cascade form consisting of a continuous time estimator,a continuous observation error predictor,and a reset compensator.The proposed ESO estimates not only the system state but also the total uncertainty,which may include the effects of the external perturbation,the parametric uncertainty,and the unknown nonlinear dynamics.Such a reset compensator,whose state is reset to zero whenever a new measurement arrives,is used to calibrate the predictor.Due to the cascade structure,the resulting error dynamics system is presented in a non-hybrid form,and accordingly,analyzed in a general sampled-data system framework.Based on the output of the ESO,a continuous ADRC law is then developed.The convergence of the resulting closed-loop system is proved under given conditions.Two numerical simulations demonstrate the effectiveness of the proposed control method.
基金co-supported by the National Natural Science Foundation of China (Nos. 51675026 and 71671009)the National Basic Research Program of China (No. 2013CB733002)
文摘The classical probabilistic reliability theory and fuzzy reliability theory cannot directly measure the uncertainty of structural reliability with uncertain variables, i.e., subjective random and fuzzy variables. In order to simultaneously satisfy the duality of randomness and subadditivity of fuzziness in the reliability problem, a new quantification method for the reliability of structures is presented based on uncertainty theory, and an uncertainty-theory-based perspective of classical Cornell reliability index is explored. In this paper, by introducing the uncertainty theory, we adopt the uncertain measure to quantify the reliability of structures for the subjective probability or fuzzy variables, instead of probabilistic and possibilistic measures. We utilize uncertain variables to uniformly represent the subjective random and fuzzy parameters, based on which we derive solutions to analyze the uncertainty reliability of structures with uncertainty distributions. Moreover, we propose the Cornell uncertainty reliability index based on the uncertain expected value and variance.Experimental results on three numerical applications demonstrate the validity of the proposed method.
