A hybrid CKF-NNPID controller for MIMO nonlinear control system

dc.contributor.authorSento, Adna
dc.contributor.authorKitjaidure, Yuttana
dc.date.accessioned2026-08-06T10:14:19Z
dc.date.available2026-08-06T10:14:19Z
dc.date.issued2016-09-06
dc.description.abstractThis 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.
dc.identifier.citation2016 13th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2016, 2016
dc.identifier.doi10.1109/ECTICon.2016.7561410
dc.identifier.other2-s2.0-84988837525
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7004
dc.source2016 13th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2016
dc.subjectadaptive learning algorithm
dc.subjectcubature Kalman filters
dc.subjectneural network PID controller
dc.subjectRobotic arm control system
dc.titleA hybrid CKF-NNPID controller for MIMO nonlinear control system
dc.typeConference Paper

Files

Collections