An event-triggered scheme is proposed to solve the problems of robust guaranteed cost control for a class of two-dimensional(2-D)discrete-time systems.Firstly,an eventtriggered scheme is proposed for 2-D discrete-time...An event-triggered scheme is proposed to solve the problems of robust guaranteed cost control for a class of two-dimensional(2-D)discrete-time systems.Firstly,an eventtriggered scheme is proposed for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities.Then,according to the Lyapunov functional method,the sufficient conditions for the existence of event-triggered robust guaranteed cost controller for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities are given.Furthermore,based on the sufficient conditions and the linear matrix inequality(LMI)technique,the problem of designing event-triggered robust guaranteed cost controller is transformed into a feasible solution problem of LMI.Finally,a numerical example is given to demonstrate that,under the proposed event-triggered robust guaranteed cost control,the closed-loop system is asymptotically stable and fewer communication resources are occupied.展开更多
A co-design scheme of event-triggered sampling mechanism and active fault tolerant control(FTC) is developed. Firstly,a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by u...A co-design scheme of event-triggered sampling mechanism and active fault tolerant control(FTC) is developed. Firstly,a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H∞constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost.Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.展开更多
Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semanti...Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information.Current datasets for event prediction,naturally,can be used for supervised learning.Event chains are either from document-level procedural action flow,or from news sequences under the same column.This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus,and adopts the standard multiple choice narrative cloze task evaluation.The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck.Based on trigger-guided structural relations in the event chains,we construct trigger evolution graph,and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy.Then there are features of two levels for each event,namely,text level semantic feature and trigger level structural feature.We design the attention mechanism to learn the features of event segments derived in term of event major subjects,and integrate relevance between event segments and the candidate event.The most possible next event is picked by the relevance.Experimental results on the real-world news corpus verify the effectiveness of the proposed model.展开更多
基金supported by the National Natural Science Foundation of China(61573129 U1804147)+2 种基金the Innovative Scientists and Technicians Team of Henan Provincial High Education(20IRTSTHN019)the Innovative Scientists and Technicians Team of Henan Polytechnic University(T2019-2 T2017-1)
文摘An event-triggered scheme is proposed to solve the problems of robust guaranteed cost control for a class of two-dimensional(2-D)discrete-time systems.Firstly,an eventtriggered scheme is proposed for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities.Then,according to the Lyapunov functional method,the sufficient conditions for the existence of event-triggered robust guaranteed cost controller for 2-D discrete-time systems with parameter uncertainties and sector nonlinearities are given.Furthermore,based on the sufficient conditions and the linear matrix inequality(LMI)technique,the problem of designing event-triggered robust guaranteed cost controller is transformed into a feasible solution problem of LMI.Finally,a numerical example is given to demonstrate that,under the proposed event-triggered robust guaranteed cost control,the closed-loop system is asymptotically stable and fewer communication resources are occupied.
基金supported by the National Natural Science Foundation of China(6147315961374136+1 种基金61104028)the Research Innovation Program of Nantong University(YKC16004)
文摘A co-design scheme of event-triggered sampling mechanism and active fault tolerant control(FTC) is developed. Firstly,a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H∞constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost.Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(71731002,71971190).
文摘Event prediction aims to predict the most possible following event given a chain of closely related context events.Previous methods based on event pairs or the entire event chain may ignore much structural and semantic information.Current datasets for event prediction,naturally,can be used for supervised learning.Event chains are either from document-level procedural action flow,or from news sequences under the same column.This paper leverages graph structure knowledge of event triggers and event segment information for event prediction with general news corpus,and adopts the standard multiple choice narrative cloze task evaluation.The topic model is utilized to extract event chains from the news corpus to deal with training data bottleneck.Based on trigger-guided structural relations in the event chains,we construct trigger evolution graph,and trigger representations are learned through graph convolutional neural network and the novel neighbor selection strategy.Then there are features of two levels for each event,namely,text level semantic feature and trigger level structural feature.We design the attention mechanism to learn the features of event segments derived in term of event major subjects,and integrate relevance between event segments and the candidate event.The most possible next event is picked by the relevance.Experimental results on the real-world news corpus verify the effectiveness of the proposed model.
