An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network

Lin, Faa-Jeng; Shieh, Po-Huang; Hung, Ying-Chih

doi:10.1049/iet-epa:20070060

Cited by 32 publications

(14 citation statements)

References 17 publications

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“…Since these components are very small, an IT2 FLC was proposed for a linear ultrasonic motor [65]. The simulation results show that the adaptive IT2 did not require prior knowledge of the plant.…”

Section: It2mentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

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Section: It2mentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Controller Applications

et al. 2016

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“…In recent years, various methods for training type-2 fuzzy neural networks have been suggested, such as genetic algorithm [16] and PSO [17]. With the expansion of research in the field of type-2 fuzzy systems, these systems have found wide applications, including the prediction of time series [18], linear motor control [19], sliding-mode control [20] and control of robots [21].…”

Section: Introductionmentioning

confidence: 99%

Hybrid fuzzy-based sliding-mode control approach, optimized by genetic algorithm for quadrotor unmanned aerial vehicles

Pazooki

Mazinan

2017

Complex Intell. Syst.

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According to the diverse applications of quadrotors in various military and space industries, this study attempts to focus on the modeling and simulation of a particular type of such robots, entitled unmanned aerial vehicles. The quadrotors are the vertical flying robots that contain four engines with impeller and it is possible to control this flyer with respect to the transitional force on these impellers. In fact, the quadrotors are the flyers with four propellers in a crisscross structure. These propellers allow the possibility of various movements to the quadrotors in such a way that control signals are applied to its operators and the difference in rotor speeds leads to the generation of different forces and movements. Such systems are dynamically unstable. Due to the nonlinear nature of quadrotor systems, modeling uncertainties and unmodeled dynamics in these systems, the need to design a suitable controller for optimal performance in different working conditions is strongly felt. In this paper, research on the modeling of unmanned flying robots in recent years is firstly reviewed and a mathematical model has been proposed for quadrotor robots with 6 degrees of freedom on the basis of the popular Newton-Euler equations. In the following, the capability of hybrid fuzzy-based sliding-mode control approach has been used to ensure the system stability. In addition, the genetic algorithm has been added to the control structure in order to optimize the proposed con-

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“…In recent years, variety of methods has been suggested for training type-2 fuzzy neural networks such as Genetic Algorithm (GA) [18] and Particle Swarm Optimization (PSO) [19]. By daily growing research on type-2 fuzzy systems, these systems have found excessive applications such as time series prediction [20], linear motor control [21], system identification and modeling [22], sliding mode control [23], pattern recognition [24], and robot control [25].…”

Section: Introductionmentioning

confidence: 99%