Fuzzy neural network vibration control on a piezoelectric flexible hinged plate using stereo vision detection

Abstract

Vibration control on a two-connected piezoelectric flexible hinged plate is investigated, using a fuzzy neural network algorithm based on binocular vision measurement. As for vision sensing, a method to acquire vibration signals of the low frequency bending and torsional mode is investigated. To damp out the residual vibration quickly, the fuzzy neural network is applied to ensure the stability and control effect adaptively. To verify the stereo vision measurement method and the applied controller, an experimental setup of the piezoelectric flexible hinged plate with a binocular stereo vision is constructed. Experiments are conducted by using the binocular stereo vision measurement system and the adopted controller. The experimental results demonstrate the feasibility of the visual measurement method. Furthermore, the designed fuzzy neural network can attenuate the bending and torsional vibrations quickly, in comparison with proportional and derivative control, particularly for the small-level residual vibration.

Variable Universe Fuzzy Control of High-Speed Elevator Horizontal Vibration Based on Firefly Algorithm and Backpropagation Fuzzy Neural Network

“…For the contraction-expansion factors 1 α and 2 α of the main controller, using the FNN control method [25]- [26], the auxiliary controller is designed. As shown in FIGURE 7, this FNN adopts a Mamdani forward network with two inputs and two outputs; c x  and v ∆ are the car horizontal acceleration input and velocity input between the car and the guide shoe, respectively.…”

“…In recent years, swarm intelligence algorithms such as particle swarm optimization [24], artificial bee colony algorithm [25] and firefly algorithm [26] have been widely studied by scholars to replace the BP algorithm. Among them, FA is a heuristic algorithm proposed by Yang in 2009 [27].…”

Intelligent control of horizontal vibration of high-speed elevator based on gas–liquid active guide shoes

“…The control principle is shown in Figure 14. The fuzzy neural network structure based on the Mamdani model [36,37] is shown in Figure 15. In the network, since the fuzzy segmentation number of each input component is predetermined, and the second layer membership function m j i selects the bell-type function represented by the Gaussian function, it is only necessary to learn three parameters: the connection weight w ij of the fifth layer, and the central value c ij and the width value s ij of the second layer membership function.…”

Design and analysis of a two-degrees-of-freedom monolithic compliant piezoelectric microgripper

“…To date, microgrippers with various kinds of actuators, including the electrostatic actuator (Boudaoud et al, 2013; Felix et al, 2021), shaped memory alloy actuator (AbuZaiter et al, 2016; Yurtsever and Küçük, 2020), electromagnetic actuator (Go et al, 2016; Xiao and Li, 2014), electrothermal actuator (Chu et al, 2011; Soma et al, 2018), and piezoelectric actuator (Chen et al, 2020; Nah and Zhong, 2007; Rakotondrabe et al, 2011), have been developed. In particular, the piezoelectric actuator is extensively used in many microgrippers because of its high resolution, fast response speed, and high output stiffness (Qiu and Wang, 2019; Zhu et al, 2016b). Nonetheless, the piezoelectric actuators have a limited stroke.…”