Open AccessA Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System
Author(s) -
Adna Sento,
Yuttana Kitjaidure
Publication year - 2017
Publication title -
ecti transactions on computer and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.132
H-Index - 2
ISSN - 2286-9131
DOI - 10.37936/ecti-cit.2016102.64772
Subject(s) - control theory (sociology) , controller (irrigation) , computer science , pid controller , open loop controller , matlab , nonlinear system , control engineering , artificial neural network , engineering , control (management) , artificial intelligence , closed loop , temperature control , physics , quantum mechanics , agronomy , biology , operating system
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.