Abstract
In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system. Firstly, a fractional order sliding surface is designed, and the fractional order terms can provide additional freedom and improve the control accuracy. Then, the upper bound of lumped nonlinearities is estimated online using a double feedback fuzzy neural network. Accordingly, the gain of switching law is replaced by the estimated value. Meanwhile, the parameters of the double feedback fuzzy network, including base widths, centers, output layer weights, inner gains, and outer gains, can be adjusted in real time in order to improve the stability and identification efficiency. Finally, the simulation results display the performance of the proposed approach in terms of convergence speed and track speed.
Highlights
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China; Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Abstract: In this paper, an adaptive double feedback fuzzy neural fractional order sliding control approach is presented to solve the problem that lumped parameter uncertainties cannot be measured and the parameters are unknown in a micro gyroscope system
An adaptive controller is utilized to update all unknown parameters of the micro gyroscope system
This paper presented a fractional-order adaptive double feedback fuzzy neural netThis paper presented a fractional-order adaptive double feedback fuzzy neural work sliding mode control method for estimating the unknown parameters of the micro network sliding mode control method for estimating the unknown parameters of the gyroscope system with system uncertainty and external disturbance
Summary
The micro gyroscope has two working modes: drive mode and sense mode. The measurement accuracy is mainly affected by the stability of the drive mode control. The dynamic model is transformed into dimensionless form to reduce the complexity of the controller design Dividing both sides of Equations (1) and (2) by the mass block m , the natural resonance frequency ω0 and the reference length q0 are used to obtain the dimensionless dynamic model as follows:. The fractional order sliding controller is received as follows:
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
