A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

Main Article Content

Adna Sento
Yuttana Kitjaidure

Abstract

This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4- joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNPID controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.

Article Details

How to Cite
[1]
A. Sento and Y. Kitjaidure, “A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System”, ECTI-CIT, vol. 10, no. 2, pp. 176-184, Mar. 2017.
Section
Artificial Intelligence and Machine Learning (AI)
Author Biographies

Adna Sento, Department of Electronics Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

adan_120 Lecture at Thai-Nichi Institute of Technology.

Yuttana Kitjaidure, Department of Electronics Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand

aj_picture_120Lecture at Department of Electronics Engineering, Faculty of Engineering King Mongkut’s Institute of Technology Ladkrabang