Field-Programmable Gate Arrays

[30] Efe MO. Fractional fuzzy adaptive sliding-mode control of a 2-DOF directdrive robot arm. IEEE Transactions on in Systems, Man, and Cybernetics, Part B: Cybernetics. 2008;**38**(6):1561-1570. DOI: 10.1109/TSMCB.2008.918227

2017;**225**:012229. DOI: 10.1088/ 1757-899X/225/1/012229

[38] Available from: https://www.math works.com/help/sldo/gs/optimizecontroller-parameters-to-meet-stepresponse-requirements-gui.html

[39] Ullah N, Shaoping W, Khattak MI, Shafi M. Fractional order adaptive fuzzy sliding mode controller for a position servo system subjected to aerodynamic loading and nonlinearities. Aerospace Science and Technology. June 2015:

[40] Ullah N, Han S, Khattak MI. Adaptive fuzzy fractional-order sliding mode controller for a class of dynamical systems with uncertainty. Transactions of the Institute of Measurement and Control. 2015:1-12. DOI: 10.1177/

381-387

0142331215587042

[31] Moreles MA, Lainez R.

*Control Theory in Engineering*

Jan 2016

ACC.2014.6858955

**133**(1):381-388

2018;**31**(5):1141-1152

10.1155/2014/871614

2016.2568262

**284**

Mathematical modeling of fractional order circuits. arXiv: 1602.03541v121.

[32] Mujumdar A, Tamhane B, Kurode S. Fractional order modeling and control of a flexible manipulator using sliding modes. In: 2014 American Control Conference, Portland, OR; 2014. pp. 2011-2016. DOI: 10.1109/

[33] Lin C, Basu B, McCabe D. Fractional order models for system identification of thermal dynamics of buildings. Energy and Buildings. December 2016;

[34] Ijaz S, Yan L, Humayun MT. Fractional order modeling and control of dissimilar redundant actuating system used in large passenger aircraft. Chinese Journal of Aeronautics. May

[35] Razminia A, Baleanu D. Fractional order models of industrial pneumatic controllers. Abstract and Applied Analysis. 2014;**2014**:9. ID 871614. DOI:

[36] Adhikary A, Sen S, Biswas K. Practical realization of tunable fractional order parallel resonator and

Transactions on Circuits and Systems I: Regular Papers. August 2016;**63**(8): 1142-1151. DOI: 10.1109/TCSI.

[37] Verma R, Pandey N, Pandey R. Electronically tunable fractional order all pass filter. IOP Conference Series: Materials Science and Engineering.

fractional order filters. IEEE

**287**

**Chapter 13**

**Abstract**

FIR filter.

**1. Introduction**

Towards Optimised FPGA

*Syed Manzoor Qasim, Mohammed S. BenSaleh* 

*and Abdulfattah M. Obeid*

Realisation of Microprogrammed

Finite impulse response (FIR) filter is one of the most common type of digital filter used in digital signal processing (DSP) applications. An FIR filter is usually realised in hardware using multipliers, adders and registers. Field programmable gate arrays (FPGAs) have been widely explored for the hardware realisation of FIR filters using different algorithms and techniques. One such technique that has recently gained considerable attention is the use of microprogrammed control unit (MPCU) in designing FIR filters. In this chapter, we further explore MPCU technique for optimised hardware realisation of digital FIR filter. To evaluate the performance, two different architectures of FIR filter are designed using Wallace tree multiplier. Both the architectures are coded in Verilog hardware description language (HDL). The performance is analysed by evaluating the resource utilisation and timing reports of Virtex-5 FPGA generated by the Synopsys Synplify Pro tool. Based on the implementation results, as compared to conventional design, Wallace tree multiplier using carry skip adder (CSKA) provides optimal digital

**Keywords:** carry skip adder, field programmable gate array (FPGA), FIR filter,

Digital filters play an important role in many digital signal processing (DSP) applications. These applications range from noise reduction, spectral shaping, equalisation, signal detection and signal analysis, etc. The basic building blocks of digital filter are adder, multiplier and register based delay elements. Based on the application requirement, these blocks are connected to realise a particular architecture of filter. There are several ways to realise digital filters. Two such filters used in different applications are finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are widely preferred for DSP applications because they are always stable, exhibit linear phase properties and provide no feedback. Convolution, the core operation of FIR filter, performed on a window of *N* data samples involves multiplication and addition. For optimal realisation of FIR filter,

microprogrammed control unit, Wallace tree multiplier

these arithmetic operation needs to be optimised.

Control Unit Based FIR Filters
