This paper investigates the problem of sliding mode control for a class of stochastic Markovian jumping systems with partially known transition rate.A key feature in this work is to relax the requirement that all the ...
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ISBN:
(纸本)9781479900305
This paper investigates the problem of sliding mode control for a class of stochastic Markovian jumping systems with partially known transition rate.A key feature in this work is to relax the requirement that all the elements in transition rate matrix are known,which is usually encountered in some existing *** is shown that the reachability of the specified sliding surface can be ensured by the designed sliding mode ***,the suffcient conditions for the stability of the sliding motion on the sliding surface are also derived.
Clustering is an energy efficient routing protocol for wireless sensor networks. Traditional clustering methods can prolong the network lifetime and achieve scalable performance, but they do not consider the event dev...
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ISBN:
(纸本)9781467374439
Clustering is an energy efficient routing protocol for wireless sensor networks. Traditional clustering methods can prolong the network lifetime and achieve scalable performance, but they do not consider the event development. In many applications, the scalability and occurrence region of events often change. A dynamic clustering method with overlaps(DCMO) is proposed in this paper. Due to the 2-logical-coverage overlaps of the proposed clustering method, the clusters can be migrated with the changing tendency of events. As a result, the sensed data can be transmitted at a lower price. Simulations show that the proposed DCMO method has lower energy consumption, compared with LEACH protocol.
In petrochemical field, the process simulation for distillation is an important task. The key parameter in the distillation process simulation is the tray efficiency, which can not be obtained easily. Thus the determi...
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The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present,...
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The paper focuses on the implementation of a model based predictive control(MBPC) method, for Continuous Stirred Tank Reactors. First, the modelling problem of a single irreversible exothermic reaction, taking place i...
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The paper focuses on the implementation of a model based predictive control(MBPC) method, for Continuous Stirred Tank Reactors. First, the modelling problem of a single irreversible exothermic reaction, taking place in a perfectly mixed continuously stirred tank reactor(CSTR) is presented. The dynamic model consists of differential material and energy balance equations. The control strategy is investigated and evaluated by performing simulations and analyzing the results. The disturbance rejection capacity of the control system(regulatory control performances) have been tested and compared with those obtained using a classical Proportional Integral Derivative(PID) based controller. The results show that this control strategy has good performances and can be efficiently used to control the CSTR.
Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's i...
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Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalograph (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and problems are discussed. Finally, the future development of brain control is analyzed and prospects are given.
In this paper, a robust fault detection and diagnosis (FDD) method is proposed for multiple-model systems with modeling uncertainties. A compensation step is introduced to modify the mixed states and their variances o...
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The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** o...
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The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.
In this brief, a fault-detection and diagnosis method is proposed for the stochastic hybrid system with the consideration of model parameter uncertainty. To negate the effect of model uncertainty, a compensation step ...
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With the widespread use of distributed systems, multi-subspace whole-flow industrial monitoring methods are evolving. However, due to the lack of distinctive features, incipient faults in plant-wide processes are more...
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ISBN:
(数字)9798350364194
ISBN:
(纸本)9798350364200
With the widespread use of distributed systems, multi-subspace whole-flow industrial monitoring methods are evolving. However, due to the lack of distinctive features, incipient faults in plant-wide processes are more difficult to detect. To improve the detection rate of incipient faults in plant-wide processes while maintaining the generality of the algorithm, a novel double-layer subspace weighted moving window reconstruction independent component analysis (DS-WRICA) method is proposed. In DS-WRICA, process variables are first divided into different subspaces based on process knowledge and data-driven partitioning methods. Secondly, a weighted moving window is used to increase the offset of incipient faults, and monitoring statistics are constructed by combining optimized reconstructed independent component analysis (RICA) and local outlier factor (LOF) in each subspace. Then, the monitoring statistics in each subspace are fused with information by Bayesian inference fusion method to obtain distributed monitoring results. Finally, the effectiveness and superiority of the DS-WRICA method are verified by industrial examples.
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