Design and simulation of parallel distributed IIR filter based on FPGA

2015 ◽  
pp. 363-367
Author(s):  
D Sun ◽  
X Li
Keyword(s):  
Iir Filter

Control loop oscillation detection and quantification using PRONY method of IIR filter design and deep neural network

2021 ◽  
pp. 1-14
Author(s):  
Sachin Sharma ◽  
Vineet Kumar ◽  
K.P.S. Rana
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Filter Design ◽  
Support Vector ◽  
Control Loop ◽  
External Interference ◽  
Iir Filter ◽  
Prony Method ◽  
Oscillation Detection ◽  
Detection And Quantification

Generally, the process industry is affected by unwanted fluctuations in control loops arising due to external interference, components with inherent nonlinearities or aggressively tuned controllers. These oscillations lead to production of substandard products and thus affect the overall profitability of a plant. Hence, timely detection of oscillations is desired for ensuring safety and profitability of the plant. In order to achieve this, a control loop oscillation detection and quantification algorithm using Prony method of infinite impulse response (IIR) filter design and deep neural network (DNN) has been presented in this work. Denominator polynomial coefficients of the obtained IIR filter using Prony method were used as the feature vector for DNN. Further, DNN is used to confirm the existence of oscillations in the process control loop data. Furthermore, amplitude and frequency of oscillations are also estimated with the help of cross-correlation values, computed between the original signal and estimated error signal. Experimental results confirm that the presented algorithm is capable of detecting the presence of single or multiple oscillations in the control loop data. The proposed algorithm is also able to estimate the frequency and amplitude of detected oscillations with high accuracy. The Proposed method is also compared with support vector machine (SVM) and empirical mode decomposition (EMD) based approach and it is found that proposed method is faster and more accurate than the later.

scholarly journals A Construction of Recurrent Neural Network IIR Filter by means of BPTT Learning using M Sequence

1998 ◽  
Vol 118 (3) ◽  
pp. 411-418
Author(s):  
Hiroki Yoshimura ◽  
Tadaaki Shimizu ◽  
Takashi Sayama ◽  
Naoki Isu ◽  
Kazuhiro Sugata
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Iir Filter ◽  
M Sequence

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

2016 ◽  
Vol 24 (6) ◽  
pp. 1086-1100
Author(s):  
Utku Boz ◽  
Ipek Basdogan
Keyword(s):  
Computational Complexity ◽  
Vibration Control ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Active Vibration

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

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