Nonlinear System Parameterization and Control using Reduced Adaptive Kernel Algorithm

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Pattanaik Rakesh Kumar; Sarfraz Muhammad; Mohanty Mihir Narayan

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Journal Paper

Paper Information

Journal: International Journal of Industrial Engineering & Production Research Year:2022 | Volume:33 | Issue:4 Page(s): 1-16

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Information Journal Paper

Title

Nonlinear System Parameterization and Control using Reduced Adaptive Kernel Algorithm

Author(s)

Pattanaik Rakesh Kumar | Sarfraz Muhammad | Mohanty Mihir Narayan | Issue Writer Certificate

Keywords

Kernel adaptive filtering

Nonlinear system indentation

Least-mean square

Kernel least-mean square

Single input

Single-output (SISO) System

Gaussian kernel

Abstract

To develop a system for specific purpose, it needs to estimate its parameters (parameterization). It can be used in different fields like engineering, industry etc. In this work, authors used adaptive algorithm to model a system that is applicable in industry for control. This adaptive model is non-linear where its estimation is based on kernel based Least-mean square (LMS) algorithm. The kernel used as Polynomial and Gaussian. As the system is nonlinear polynomial kernel-based algorithm fails to prove its efficacy, though it is of low complexity approach. Gaussian kernel-based application for nonlinear system control performance better as compared to polynomial kernel. Further its complexity is reduced and used for faster performance. The result shows its performance in form of MSE, MAE, RMSE for identification and control that is very useful in industrial application.

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