International Journal of System Control and Information Processing
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International Journal of System Control and Information Processing (5 papers in press)
Non-fragile event-triggered control of positive switched systems by Yanqi Wu, Junfeng Zhang Abstract: This paper presents a non-fragile event-triggered control approach to positive switched systems without/with input saturation. First, a 1-norm based on event-triggered mechanism is established for positive switched systems. By using the matrix decomposition technique, a non-fragile controller based on event-triggered mechanism is designed for positive switched systems without saturation. Then, the presented non-fragile event-triggered control design approach is developed to positive switched systems with saturation.\r\nThe saturation term is transformed into interval form and a controller is designed by means of the linear programming. The positivity and stability of the closed-loop systems are guaranteed under the designed controllers. Finally, a numerical example is given to illustrate the effectiveness of the design. Keywords: Event-triggered mechanism; Non-fragile control; Positive switched systems; Linear programming.
Controller Design via Model Order Reduction for Interval Systems Using Kharitonov Theorem and Nevalinna-Pick Theory: A Case Study by Dharma Pal Singh Chauhan, Vinay Pratap Singh Abstract: This manuscript describes a new procedure to design a controller for unstable high order interval systems using model order reduction and Nevalinna-pick theory. In this process, First, a high-order unstable intervalsystem is converted into a reduced-order model in which improved Routh table truncation and time-moment matching techniques are applied. The denominator and numerator of reduced- order model are evaluated by improved Routh table truncation and time-moment matching technique respectively. Next, employing the Kharitonov theorem and Nevalinna-pick theory, controller is designed for a reduced-order model in a certain range of uncertainty. To explain the procedure,the proposed method is applied to the fourth-order unstable interval system. Results prove the effectiveness of the method. Keywords: Controller design; Interval system; Kharitonov theorem; Model order reduction; Nevallina-pick theory.
Soft Measurement of Dioxin Emission Concentration Based on Deep Forest Regression Algorithm by Jian Tang, Heng Xia, Jun Fei Qiao, Zi Hao Guo Abstract: Dioxin (DXN) is an organic pollutant emitted by the municipal solid waste incineration (MSWI) process. It is called the \"poison of the century\" and is one of the main reasons that cause of the \"not in my backyard\" problem. In actual industrial process, the DXN emission concentration is detected by using offline laboratory analysis method with a monthly/seasonal or un-determined period. Thus, it is difficult to provide effective support for the operation optimization of the MSWI process. In this paper, a soft measurement method of DXN emission concentration based on deep forest regression (DFR) algorithm is proposed. First, the input layer forest model consists of multiple sub-forest models is trained and the layer regression vector is obtained. Then, the augmented layer regression vector that serial combine the layer regression vector with the raw features is used to train the middle layer forest model, and its depth is adaptively adjusted through the validation error. Finally, the augmented layer regression vector of the middle layer forest is fed into the output layer forest model to make the final prediction. Therefore, the final DXN soft measurement model based on DFR algorithm is obtained. The effectiveness of the proposed method was verified by the cement compressive strength data of UCI benchmark platform and the DXN emission concentration data of the actual MSWI process. Keywords: Dioxin emission concentration; Deep forest regression (DFR); Layer regression vector; Augmented layer regression vector; Municipal solid waste incineration (MSWI).
A Variational Autoencoders Approach for Process Monitoring and Fault Diagnosis by Peng Tang, Kaixiang Peng, Jie Dong, Kai Zhang, Ruihua Jiao Abstract: Probabilistic models, which can model the process noise and can handle the problem of missing data in the probabilistic framework, recently have been got much attention in process monitoring and fault diagnosis area. This paper presents a new probabilistic methodology for fault detection and diagnosis in nonlinear processes using a Variational Autoencoders (VAE) models. Two statistic index, based on the probability density distribution of measure variables and latent structure variable, are built to monitoring fault. Then a probabilistic contribution analysis method, based on the concept of missing variable estimation, is proposed for fault diagnosis. The performance of fault detection and diagnosis is demonstrated through its application for the monitoring of TE industrial process, and the effectiveness is verified. Keywords: VAE; process monitoring and fault diagnosis; probabilistic contribution analysis; nonlinear processes; TE.
Improved Approximation of SISO and MIMO Continuous Interval Systems by P.D. Dewangan, V.P. Singh, S.L. Sinha Abstract: Recently, some engineering systems are modelled as interval systems. In this investigation, an improved approximation method is first presented for reducing the order of single-input-single-output (SISO) continuous interval systems and then the same method is extended for reducing the order of multi-input-multi-output (MIMO) continuous interval systems utilizing multi-point Pad Keywords: Interval systems; model reduction; multipoint Padé method; system modelling.