The capability of ADRC is studied for linear time-invariant SISO minimum-phase systems with unknown orders, uncertain relative degrees, and unknown input disturbances. It is proved that ADRC can reject the unknown inp...The capability of ADRC is studied for linear time-invariant SISO minimum-phase systems with unknown orders, uncertain relative degrees, and unknown input disturbances. It is proved that ADRC can reject the unknown input disturbance and guarantee the close-loop stability for the plants with unknown but bounded relative degrees. Meanwhile, some close-loop performances can be achieved. The influence of the sensor noise is also discussed. And it is demonstrated by numerical examples that one ADRC with fixed parameters can be applied to a group of plants of different orders, relative degrees, and parameters.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 Natural Science Foundation of China under Grant Nos.60821091 and 60736022
文摘The capability of ADRC is studied for linear time-invariant SISO minimum-phase systems with unknown orders, uncertain relative degrees, and unknown input disturbances. It is proved that ADRC can reject the unknown input disturbance and guarantee the close-loop stability for the plants with unknown but bounded relative degrees. Meanwhile, some close-loop performances can be achieved. The influence of the sensor noise is also discussed. And it is demonstrated by numerical examples that one ADRC with fixed parameters can be applied to a group of plants of different orders, relative degrees, and parameters.
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.
