Time domain simulations of constant phase elements using IIR filter

2011 ◽  
pp. 17-28
Keyword(s):  
Time Domain ◽  
Iir Filter ◽  
Constant Phase Elements

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.

Time-Domain Modeling of Constant Phase Elements for Simulation of Lithium Battery Behavior

2019 ◽  
Vol 34 (8) ◽  
pp. 7573-7587 ◽  
Author(s):  
Chun-Sing Cheng ◽  
Henry Shu-Hung Chung ◽  
Ricky Wing-Hong Lau ◽  
Kelvin Yi-Wen Hong
Keyword(s):  
Time Domain ◽  
Lithium Battery ◽  
Domain Modeling ◽  
Time Domain Modeling ◽  
Constant Phase Elements

FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Aladin Kapić ◽  
Rijad Sarić ◽  
Slobodan Lubura ◽  
Dejan Jokić
Keyword(s):  
Transfer Function ◽  
Impulse Response ◽  
Discrete Time ◽  
Time Domain ◽  
Digital Filters ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Main Role ◽  
Digital Implementation ◽  
Iir Filter

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

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