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Some Investigations on Fuzzy P+Fuzzy I+Fuzzy D Controller for non-stationary process

International Journal of Automation and computing 2012年第5期9卷 449-458页

作者： Vineet Kumar B. C. Nakra A. P. Mittal Division of Instrumentation and Control Engineering Netaji Subhas Institute of Technology Delhi University Department of Mechanical Engineering Institute of Technology and Management University Division of Instrumentation and Control Engineering Netaji Subhas Institute of TechnologyDelhi University

The paper addresses the adaptive behaviour of parallel fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller. The parallel FP+FI+FD controller is actually a non-linear adaptive controller whose gain changes continuously with output of the process under control. Two non-stationary processes, whose characteristics change with time, are considered for simulation study. Simulation is performed using software LabVIEW TM . The set-point tracking response of parallel FP+FI+FD is compared with conventional parallel proportional plus integral plus derivative (PID) controller, tuned with the Ziegler-Nichols (Z-N) tuning technique. Simulation results show that conventional PID controller fails to track the set-point and becomes unstable as the process changes its characteristic with time. But the parallel FP+FI+FD controller shows considerably much better set-point tracking response and does not deviate from steady state. Also, a huge spike is observed in the output of PID controller as the reference set-point and process parameters are changed, while the FP+FI+FD controller gives spike free control signal.

关键词： Proportional plus integral plus derivative (PID) controller fuzzy proportional plus fuzzy integral plus fuzzy derivative (FP+FI+FD) controller non-stationary process adaptive control self-tuning

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A subspace analysis method combining cointegration analysis and dynamic latent variables for non-stationary process fault detection

A subspace analysis method combining cointegration analysis ...

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第35届中国过程控制会议

作者： Keyu Yao Yuguo Yang Hongbo Shi School of Information Science and Engineering East China University of Science and Technology

In the field of fault detection, changes in the normal state of certain parameters due to changes in actual operating conditions are very similar to those caused by faults. This results in certain normal process changes being misrecognized as process abnormalities, while certain process abnormalities that actually occur are masked by normal process changes. In order to solve the above problems, a subspace analysis method based on cointegration analysis and dynamic latent variables subspace analysis(CA-DLVSA) is proposed in this work. First, the process variables in the real industrial process are categorized into non-stationary and stationary variables using the ADF test. Second, the variables determined to be static are divided into the dynamic stationary subspace and static stationary subspace using the DLV model. For variables judged to be non-stationary, the non-stationary subspace is further divided into the dynamic stationary and static non-stationary subspace using an algorithm that combines CA and DLV. Then, the dynamic stationary spaces obtained from the stationary and non-stationary data are then combined to form the final dynamic stationary space and the static stationary portion of the stationary data is retained to form the final static stationary space. Finally, the corresponding statistics are constructed for process monitoring in both spaces. In three-phase flow process, the excellent performance of the proposed method in non-stationary process monitoring is verified by comparing the DLV, SFA, CVA, CA-DLVSA methods.

关键词： Cointegration analysis Dynamic latent variables process Monitoring non-stationary process

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Inverse Problem for a Time-Series Valued Computer Simulator via Scalarization

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Open Journal of Statistics 2016年第3期6卷 528-544页

作者： Pritam Ranjan Mark Thomas Holger Teismann Sujay Mukhoti OM & QT Indian Institute of Management Indore Indore India Department of Mathematics & Statistics Acadia University Wolfville Canada

For an expensive to evaluate computer simulator, even the estimate of the overall surface can be a challenging problem. In this paper, we focus on the estimation of the inverse solution, i.e., to find the set(s) of input combinations of the simulator that generates a pre-determined simulator output. Ranjan et al. [1] proposed an expected improvement criterion under a sequential design framework for the inverse problem with a scalar valued simulator. In this paper, we focus on the inverse problem for a time-series valued simulator. We have used a few simulated and two real examples for performance comparison.

关键词： Calibration Computer Experiments Contour Estimation Gaussian process Model non-stationary process Sequential Design

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Time-dependent Power Spectral Density Estimation of AE signals of Rock Based on Wavelet Transform

Time-dependent Power Spectral Density Estimation of AE signa...

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2011 Chinese Control and Decision Conference（CCDC）

作者： KANG Yumei1,ZHU Wancheng1,BAI Quan2,WU JinLin1 1(College of Resources and Civil Engineering,Northeastern University,Shenyang,Liaoning 110004,China2(School of Architecture and Civil Engineering,Shenyang University of Technology,Shenyang,Liaoning 110870 China)

Understanding and cognition on characteristics of acoustic emission （AE） signals is the foundation and precondition of read comprehensively AE *** Fourier amplitude spectrum and power spectral density（PSD） representations based on Fourier transform have limitations,that is,it loses any information related to time and is unable to completely reflect the non-stationary characteristics of AE signals *** this paper,based on wavelet transform,the relationship between wavelet coefficient in every frequency bands and power spectral density is derived;PSD estimate method and time-dependent PSD estimate method based on wavelet transform are also presented in this *** feasibility of the presented method is illustrated by AE Signal of rock processing and analysis.

关键词： acoustic emission (AE) signals wavelet transform time-dependent power spectral density (PSD) non-stationary process

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