Trajectory tracking of a 2-DOF helicopter system using neuro-fuzzy system with parameterized conjunctors

Aras, Ayse Cisel; Kaynak, Okyay

doi:10.1109/aim.2014.6878099

Cited by 22 publications

(6 citation statements)

References 10 publications

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“…The results are presented in Table 2, it is easy to notice that the three-inputs NFC has better settling time and its Root Mean Square (RMSE) is smaller than two-input NFC. It is noticed that the pitch angle RMSE is nearly same as stated in [33] but yaw angle RMSE is very different from the LQR+I controller results.…”

Section: Simulation Results and Discussionmentioning

confidence: 57%

“…17 in the tenth and twentieth seconds there are some When compared the two and three input NFC, it is stated in [33] that two input (error, error derivative) NFC controller performance on 2 DOF helicopter is close to classical PID. Besides, the two input NFC has some unexpected behaviors [33]. The same unexpected behaviors are seen in our two input NFC controllers.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The nonlinear dynamics equations can be given in terms of pitch angle and yaw angle [33].The parameters of the model are given in Table 1. (1)…”

Section: System Dynamicsmentioning

confidence: 99%

“…Another applied example is tested on the 2 DOF helicopter by using FLC with Sliding Mode Control (SMC) [31,32]. Because of NFC's high efficiency, Type1 NFC has applied alone to 2 DOF helicopter system, as in [33], with two NFC inputs having error and its derivatives.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

Öztürk

Özkol

2021

SN Appl. Sci.

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In this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

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Section: Simulation Results and Discussionmentioning

confidence: 57%

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The nonlinear dynamics equations can be given in terms of pitch angle and yaw angle [33].The parameters of the model are given in Table 1. (1)…”

Section: System Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

Öztürk

Özkol

2021

SN Appl. Sci.

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“…Any R(z -1 ) and S(z -1 ) which lead to the desirable poles in (23) can be used to form (21). Equation ( 23) can be written as (24): B(z -1 ) R(z -1 )=-A(z -1 ) S(z -1 ).…”

Section: -2 Solution Of the Control Problemmentioning

confidence: 99%

A Discrete Approach to Feedback Linearization, Yaw Control of An Unmanned Helicopter

et al. 2022

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Nonlinear control laws often need to be implemented with digital hardware. Use of digital control systems leads to communication/processing delays which are widely neglected in control of mechanical systems. This paper proposes a discrete approach to feedback linearization that considers these commonly overlooked delays in design. The proposed approach is shown to both improve the performance and remove the need for continuous derivative terms. In feedback linearization control systems, designed in the continuous domain, derivative terms are required to speed up the control response of mechanical systems, but disadvantageously cause high sensitivity to noise. The proposed approach was used to design a feedback linearization control system for a common turning maneuver of an unmanned helicopter in yaw. At this maneuver, the helicopter centroid motion and pitch rotational speed are almost zero. Governing differential equations of the helicopter at this maneuver are nonlinear and coupled. A feedback linearization law was proposed to curb nonlinearity and, a discrete control system, considering the inevitable delay due to the use of digital control systems, was adopted to complete the control law. This innovative approach resulted in less sensitivity to noises and performance boost. Practical limits in terms of control input, rotor speed, sampling frequency and noises of the gyroscope, the tachometer and the acceleration sensor were taken into account in this research. The results show that the proposed control system leads to fast and smooth yaw turns even at a high pitch angle (close to vertical) or in the case of being hit by external objects.

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Performance Analysis of a Twin Rotor MIMO System Using Fuzzy Logic Controller

Das

Bhutia

Mehta

et al. 2020

Intelligent Computing Applications for Sustainable Real-World Systems

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Trajectory tracking of a 2-DOF helicopter system using neuro-fuzzy system with parameterized conjunctors

Cited by 22 publications

References 10 publications

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

A Discrete Approach to Feedback Linearization, Yaw Control of An Unmanned Helicopter

Performance Analysis of a Twin Rotor MIMO System Using Fuzzy Logic Controller

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