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A hybrid CKF-NNPID controller for MIMO nonlinear control system
Author(s)
Sento, Adna
Kitjaidure, Yuttana
Date Issued
September 6, 2016
Type
Conference Paper
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-NNIPD 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.
Citation
2016 13th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2016, 2016