PID self-tuning method based on deep belief network and improved firefly algorithm Online publication date: Wed, 12-May-2021
by Lingzhi Yi; Xiu Xu; Mao Tan; Zongguang Zhang; Weihong Xiao; Lv Fan
International Journal of Automation and Control (IJAAC), Vol. 15, No. 3, 2021
Abstract: In order to overcome the difficulty of tuning the proportion integration differentiation (PID) parameters, a PID parameter self-tuning method based on the firefly algorithm improved by Newton's law of universal gravitation (LOGFA) and deep belief network (DBN) is proposed. Compared with the FA, LOGFA cannot only maintain the evolutionary advantage of the original algorithm but also can effectively improve the accuracy and convergence ability of the algorithm. The advantage of DBN is to train each layer of neural network separately, which greatly improves the training efficiency and accuracy. The closed-loop PID speed control system of a three-phase asynchronous motor is used as the simulation object for PID parameter self-tuning. The proposed LOGFA-DBN is compared with other three algorithms. Simulation results show that the algorithm combining LOGFA and DBN can realise the off-line parameter tuning which is not subject to the controlled object, and speed up the parameter tuning.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Automation and Control (IJAAC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook