作者：
Yu CheZhou TianBoping LiuState Key Laboratory of Chemical Engineering
East China University of Science and Technology Meilong Road 130 Shanghai 200237 Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 P.R.China
In this paper, self-triggered consensus control problem is investigated for a class of multi-agent systems. Each agent computes its next update time instance at the predefined time. This control strategy extends subst...
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ISBN:
(纸本)9781479913329;9781479940998
In this paper, self-triggered consensus control problem is investigated for a class of multi-agent systems. Each agent computes its next update time instance at the predefined time. This control strategy extends substantially the existing work on event-triggered control, because in the self-triggered deployment the agents have no need to maintain the track of the state error that triggers the actuation between continuous update instants. A distributed self-triggered control procedure is presented for a special first-order multi-agents and is shown to achieve the rendezvous objective. The effectiveness of the proposed control method is illustrated by a numerical example.
This paper studies the state feedback stabilization of LTI plants over wireless block-fading channels. The packet-loss rate of each channel depends on power level and packet length used for transmission as well as cha...
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This paper studies the state feedback stabilization of LTI plants over wireless block-fading channels. The packet-loss rate of each channel depends on power level and packet length used for transmission as well as channel power gain. Different from the case of fixed packet-loss rate, the packet-loss rate considered in this paper is random and time-varying as the channel power gain is so for wireless communication. When power level and packet length are assumed to be time-invariant for every transmission at each channel, necessary and sufficient conditions for mean square stabilizability via state feedback are given in terms of the unstable poles of the plant. The performance defined as the asymptotic mean square norm of the system state is also discussed for the scalar case.
An automatic reference selection method with prejudgments was developed in order to identify the two patterns of diffuse and localized EEG activity,as well as the topographical *** diffuse activity is identified and t...
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ISBN:
(纸本)9781467329705
An automatic reference selection method with prejudgments was developed in order to identify the two patterns of diffuse and localized EEG activity,as well as the topographical *** diffuse activity is identified and the effect of adopted reference is analyzed by using two *** localized activity is analyzed where the focal and distributed electrodes are derived based on an iterative detection ***,the suitable reference is determined which is adaptive to the actual characteristics of diffuse and localized ***,the topographical distribution is obtained automatically during the reference selection *** presented method has the performance to be an assistant and subjective tool for clinical application,especially for the quantitative interpretation on EEG rhythm.
Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz *** this study,a new filter of different frequency characteristics of EEG signal under +Gz acceleration is construc...
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ISBN:
(纸本)9781467329705
Filtering and feature extraction are very important in the analysis and study of EEG signal under +Gz *** this study,a new filter of different frequency characteristics of EEG signal under +Gz acceleration is constructed and four kinds of rhythms of EEG signal are extracted by using wavelet packet *** under different G loads is analyzed and compared,and then EEG dynamic characteristics are studied in order to analyze its advantages and *** results show that wavelet packet method can effectively suppress interference bands in EEG,such as EMG,power and so on,and effectively reflect the dynamic characteristics of different rhythms,exhibiting good *** proposed method is also applicable for analyzing and studying other dynamic biomedical signals.
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.
In this paper, the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive control. Our objective is to design the distributed impulsive controller with minimu...
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In this paper, the synchronization of a nonlinear network with time-varying coupling delay is investigated via distributed impulsive control. Our objective is to design the distributed impulsive controller with minimum coupling strength such that the nonlinear network with coupling delay is globally exponentially synchronous. Some sufficient conditions have been derived based on Lyapunov-Razumikhin method in terms of matrix inequalities. An example is presented to illustrate the effectiveness of the proposed control methods.
For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring st...
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For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.
The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the produc...
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The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.
This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control directions. By using a linear state transformation, the unknown control coeff...
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This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control directions. By using a linear state transformation, the unknown control coefficients are lumped together, such that the original system is transformed to a new system for which control design becomes feasible. By employing the input-driven observer, a novel adaptive neural network (NN) output-feedback controller which only contains one adaptive parameter is developed for such systems by using backstepping technique and NNs' parameterization. The proposed control design guarantees that all the signals in the closed-loop systems are 4-moment semi-globally uniformly ultimately bounded.
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