Interval Type-2 Fuzzy Sliding Mode Controller Based on Nonlinear Observer for a 3-DOF Helicopter with Uncertainties

Zeghlache, Samir; Benslimane, Tarak; Amardjia, Nourredine; Bouguerra, Abderrahmen

doi:10.1007/s40815-016-0226-5

Cited by 16 publications

(9 citation statements)

References 37 publications

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“…A survey was conducted to illustrate the advantages of hybrid type-II fuzzy SMC in Hamza et al, 132 and it was noted that type-II fuzzy systems have been widely used in recent years to control systems with high noise and uncertainties. However, only a few studies have been published on the application of type-II fuzzy systems to underactuated systems, including interval type-II fuzzy sliding-mode controllers, 92–94 interval type-II fuzzy PD controllers, 71,75 interval type-II fuzzy fault-tolerant controllers, 133 hybrid interval type-II fuzzy controllers, 13 the optimization of interval type-II fuzzy logic controllers, 126 and interval type-II FNN controllers. 35 The type-II fuzzy systems in the above studies are used in simple applications, and their fuzzy rules are designed based on human experience and knowledge.…”

Section: Resultsmentioning

confidence: 99%

“…One sliding model control system was used to design the controller for each subsystem, and an interval type-II fuzzy system simplified by a new reduction method applied to learn the switching control law, which is a discontinuous function. A coaxial trirotor aircraft in Zeghlache et al 93 and a 3-DOF helicopter in Zeghlache et al 94 were analyzed using a method similar to that in Tao et al 92 to design the controllers. However, the coaxial trirotor aircraft and the 3-DOF helicopter were decoupled into six subsystems and three subsystems, respectively, and the Karnik–Mendel algorithm was used as the type-reduction method in Zeghlache et al 93,94…”

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

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A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Underactuated systems represent a class of mechanical systems with fewer independent control actuators than degrees of freedom. The control problem of underactuated systems is one of the most challenging in the theory of nonlinear control because such systems exhibit strong coupling and highly nonlinear characteristics. Fuzzy systems have been successfully applied to control underactuated systems in both theoretical analysis and practical application. This survey describes the model and application of underactuated systems and classifies fuzzy systems in a straightforward manner. Moreover, this study analyzes problems facing conventional control methods and presents the development of fuzzy systems to solve these problems.

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Section: Resultsmentioning

confidence: 99%

Section: Control Methods Based On Fuzzy Systems For Underactuated Sysmentioning

confidence: 99%

A survey on the application of fuzzy systems for underactuated systems

Huang

Ralescu

Gao

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…Neural fuzzy control [193][194][195][196][197][198][199] Fuzzy neural network control 200,202,[294][295][296] Optimization methods for fuzzy control 203,297-304 PFL Collocated PFL 8,210,[305][306][307][308][309] Non-collocated PFL 211,[310][311][312][313] Energy-based methods 19,215,216,226, SMC SMC with a model-free fuzzy system [243][244][245][246][247][248]252,257,[259][260][261][262]265,267,268,270,271,303,337 Adaptive SMC with a direct or indirect fuzzy system 205,…”

Section: Related Studies Primary Classification Secondary Classificationmentioning

confidence: 99%

Underactuated robotics: A review

Wang

Liu

2019

International Journal of Advanced Robotic Systems

146

102

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Underactuated robotics is an emerging research direction in the field of robotics. The control input of the underactuated robot is less than the degree of freedom of the system. It has the advantages of lightweight, low energy consumption, excellent performance, and broad development prospects. This article reviews the state of the art on underactuated robotics. On the basis of previous studies, this article takes the non-holonomic constraint equation as the entry point to classify and summarize underactuated robot and their common mechanisms. The controllability of underactuated robot is further discussed. The control flow of underactuated robot is described based on the open-closed control method. In the closed-loop control, the control method based on the fuzzy system is mainly used. Finally, the difficulties in the current research of underactuated robot are summarized, and the future research directions are prospected.

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“…The slip ring mechanism on the vertical axis allows the body to rotate continuously by eliminating the need for any wires to connect the motors and encoders to the base. The front and back propellers control the movement of the helicopter [20][21][22][23][24]. The 3-DOF helicopter principals to control the three axis with front and back propellers, which control the movement of the helicopter.…”

Section: Adaptive Controlmentioning

confidence: 99%

Adaptive control of nonlinear system based on QFT application to 3-DOF flight control system

et al. 2019

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Research on unmanned aerial vehicle (UAV) became popular because of remote flight access and cost-effective solution. 3-degree of freedom (3-DOF) unmanned helicopters is one of the popular research UAV, because of its high load carrying capacity with a smaller number of motor and requirement of forethought motor control dynamics. Various control algorithms are investigated and designed for the motion control of the 3DOF helicopter. Three-degree-of-freedom helicopter model configuration presents the same advantages of 3-DOF helicopters along with increased payload capacity, increase stability in hover, manoeuvrability and reduced mechanical complexity. Numerous research institutes have chosen the three-degree-of-freedom as an ideal platform to develop intelligent controllers. In this research paper, we discussed about a hybrid controller that combined with Adaptive and Quantitative Feedback theory (QFT) controller for the 3-DOF helicopter model. Though research on Adaptive and QFT controller are not a new subject, the first successful single Adaptive aircraft flight control systems have been designed for the U.S. Air Force in Wright Laboratories unmanned research vehicle, Lambda [1]. Previously researcher focused on structured uncertainties associated with controller for the flight conditions theoretically. The development of simulationbased design on flight control system response, opened a new dimension for researcher to design physical flight controller for plant parameter uncertainties. At the beginning, our research was to investigates the possibility of developing the QFT combined with Adaptive controller to control a single pitch angle that meets flying quality conditions of automatic flight control. Finally, we successfully designed the hybrid controller that is QFT based adaptive controller for all the three angles.

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Interval Type-2 Fuzzy Sliding Mode Controller Based on Nonlinear Observer for a 3-DOF Helicopter with Uncertainties

Cited by 16 publications

References 37 publications

A survey on the application of fuzzy systems for underactuated systems

A survey on the application of fuzzy systems for underactuated systems

Underactuated robotics: A review

Adaptive control of nonlinear system based on QFT application to 3-DOF flight control system

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