FPGA Design and Implementation Issues of Artificial Neural Network Based PID Controllers

Author

Gupta, Vikas ; Khare, K. ; Singh, R.P.

Author_Institution

Dept. of Electron. & Telecommun., Vidyavardhani´´s Coll. of Eng., Thane, India

fYear

2009

fDate

27-28 Oct. 2009

Firstpage

860

Lastpage

862

Abstract

This paper discusses implementation issues of FPGA and ANN based PID controllers. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. This paper discusses the issues involved in implementation of a multi-input neuron with linear/nonlinear excitation functions using FPGA. It also suggests advantages of error self-recurrent neural networks over back propagation neural network.

Keywords

backpropagation; field programmable gate arrays; integrated circuit design; neural nets; reconfigurable architectures; three-term control; FPGA design; PID controller; artificial neural network; backpropagation neural network; error self-recurrent neural network; multiinput neuron; nonlinear excitation function; reconfigurable computing architecture; Artificial neural networks; Communication system control; Computer networks; Control systems; Field programmable gate arrays; Hardware; Multi-layer neural network; Neural networks; Neurons; Three-term control; FPGA; PID controller; artificial neural networks; error self-recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on

Conference_Location

Kottayam, Kerala

Print_ISBN

978-1-4244-5104-3

Electronic_ISBN

978-0-7695-3845-7

Type

conf

DOI

10.1109/ARTCom.2009.182

Filename

5328349