This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetit...
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This paper addresses the problem of robust iterative learning control design for a class of uncertain multiple-input multipleoutput discrete linear systems with actuator faults. The stability theory for linear repetitive processes is used to develop formulas for gain matrices design, together with convergent conditions in terms of linear matrix inequalities. An extension to deal with model uncertainty of the polytopic or norm bounded form is also developed and an illustrative example is given.
In this paper,we consider fast and desired consensus of directed network via pinning ***,we provide a sufficient condition for the stability of desired consensus ***,we investigate the problem of selecting optimal pin...
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ISBN:
(纸本)9781479900305
In this paper,we consider fast and desired consensus of directed network via pinning ***,we provide a sufficient condition for the stability of desired consensus ***,we investigate the problem of selecting optimal pinned nodes for driving fastest consensus,which is formulated as an Mixed-Integer Semidefinite ***,we illustrate all the results by simulating on some typical directed networks.
Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compres...
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Cracking gas compressor is usually a centrifugal compressor. The information on the performance of a centrifugal compressor under all conditions is not available, which restricts the operation optimization for compressor. To solve this problem, two back propagation (BP) neural networks were introduced to model the performance of a compressor by using the data provided by manufacturer. The input data of the model under other conditions should be corrected according to the similarity theory. The method was used to optimize the system of a cracking gas compressor by embedding the compressor performance model into the ASPEN PLUS model of compressor. The result shows that it is an effective method to optimize the compressor system.
The WirelessHART network is an emerging technology which aims at application in industry. The control network in industry always consists of many end devices and the environment is hash, so the network management of W...
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In this paper, the disturbance decoupling observer design problem for a class of distributed parameter systems with unknown disturbance in the state and in the measurement equations is considered. The disturbance dist...
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ISBN:
(纸本)9781479947249
In this paper, the disturbance decoupling observer design problem for a class of distributed parameter systems with unknown disturbance in the state and in the measurement equations is considered. The disturbance distribution is an unknown signal, but the operator describing the disturbance is known. All operators for systems and observers are bounded operators.A novel disturbance decoupling observers design scheme is proposed via the algebraic transformation. The solvability of the problem hinges on the solution to a corresponding operator equation. The existence conditions for the disturbance decoupling observers are presented. Finally, a numerical example of a 1-D parabolic system is used to illustrate the results of the disturbance decoupling observers..
Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on ri...
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Fault monitoring of bioprocess is important to ensure safety of a reactor and maintain high quality of products. It is difficult to build an accurate mechanistic model for a bioprocess, so fault monitoring based on rich historical or online database is an effective way. A group of data based on bootstrap method could be resampling stochastically, improving generalization capability of model. In this paper, online fault monitoring of generalized additive models （GAMs） combining with bootstrap is proposed for glutamate fermentation process. GAMs and bootstrap are first used to decide confidence interval based on the online and off-line normal sampled data from glutamate fermentation experiments. Then GAMs are used to online fault monitoring for time, dissolved oxygen, oxygen uptake rate, and carbon dioxide evolution rate. The method can provide accurate fault alarm online and is helpful to provide useful information for removing fault and abnormal phenomena in the fermentation.
Brain computer interface (BCI) could help patients to manipulate external devices based on the specific brain activities. One of the most popular BCI systems is the visual-based BCI system. Mostly, users were asked to...
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This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instant...
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This paper is concerned with the stochastic bounded consensus tracking problems of leader-follower multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, and the measurements are corrupted by random noises. The probability limit theory and the algebra graph theory are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. It is shown that the maximum allowable upper boundary of the sampling period simultaneously depends on the constant feedback gains and the network topology. Furthermore, the effects of the sampling period on the tracking performance are analysed. It turns out that from the view point of the sampling period, there is a trade-off between the tracking speed and the static tracking error. Simulations are provided to demonstrate the effectiveness of the theoretical results.
The detection of blade icing faults in wind farms is an important task in improving the reliability and safety of wind power systems. Detection is primarily achieved through supervised learning, using labeled samples....
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In this paper, a quantized H∞ control problem for networked control systems (NCSs) subject to randomly multi-step transmission delays is investigated. A quantizer is used before the measurement signal enters the comm...
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