Trajectory tracking in aircraft landing operations management using the adaptive neural fuzzy inference system

Liu, D. M.; Naadimuthu, G.; Lee, E. S.

doi:10.1016/j.camwa.2007.11.052

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…This paper is based on fuzzy logic, granular computing and control area [3][4][5][6][7][8]10], when two or more different areas work together to solve any problem, results obtained can be better [12][13][14][15][16]. We explain and illustrate the proposed approach with a benchmark problem, this case of control [17,18,[21][22][23] is an example of a simulation plant with two DC Motors.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Intelligent Control of Nonlinear Dynamic Plants Using a Benchmark Problem

Cervantes

Castillo

2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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In this paper an approach for control is illustrated, this method is applied in complex control problems, and this is the main objective to use an approach to solve complex control problems and obtain betters results. In this paper simulation results are presented when the proposed method is applied using a benchmark problem. The first part in this paper shows the proposed method for intelligent control combining the outputs of the modules, then the approach is illustrated using a problem with 2 DC motors in a simulation plant, the proposed method shows how we can achieve the total control of the case of study.

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Section: Introductionmentioning

confidence: 99%

A New Approach for Intelligent Control of Nonlinear Dynamic Plants Using a Benchmark Problem

Cervantes

Castillo

2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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“…In [4] Neuro-fuzzy based fuzzy subtractive clustering method has been used to control the TRMS in both vertical and horizontal planes. Trajectory tracking in aircraft landing operations management using ANFIS has been presented in [5]. The approach has been illustrated by the use of both zero and first order-Takagi-Sugeno inference systems.…”

Section: Introductionmentioning

confidence: 99%

An on-line approach for ANFIS modelling and control of a flexible manoeuvring system

Omar

Mohamad

Zaidan

et al. 2011

2011 IEEE International Conference on Mechatronics

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This paper presents an on-line nonlinear dynamic modelling and control approach based on adaptive neuro-fuzzy inference system (ANFIS) for a twin rotor multi-input multi output system (TRMS), in the vertical plane motion. The TRMS can be considered as a flexible aerodynamic test rig that resembles the behaviour of a helicopter in hovering mode. The TRMS and similar manoeuvring systems are often subjected to random disturbances arising from various sources such as driving motors and external environmental sources. For such highly nonlinear systems with varying operating conditions, adaptive control approaches are suitable tools to cope with plant uncertainties. A model inverse control of the TRMS is developed using on-line ANFIS learning algorithm. The consequent and antecedent parameters of a fI rst order Takagi-Sugeno fuzzy inference system are optimised on-line using recursive least squares and gradient descent algorithms, respectively. The optimal initialization of the ANFIS parameters is achieved through an off-line training process. The developed strategy is compared to other control laws in terms of tracking performance and disturbance rejection. The obtained simulation results demonstrate the efficiency of the on-line control scheme.

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“…In literature, there are different off-line ANFIS controller studies such as tuning PID coefficients [10,11], determining FLC parameters [12] using ANFIS inverse controller by modelling the inverse of system [13]. Off-line ANFIS trained controllers are used in different areas such as robotics [14], aircraft [15] etc. However, these off-line controllers are ineffective against changes in the system and environment.…”

Section: Introductionmentioning

confidence: 99%

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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Trajectory tracking in aircraft landing operations management using the adaptive neural fuzzy inference system

Cited by 15 publications

References 12 publications

A New Approach for Intelligent Control of Nonlinear Dynamic Plants Using a Benchmark Problem

A New Approach for Intelligent Control of Nonlinear Dynamic Plants Using a Benchmark Problem

An on-line approach for ANFIS modelling and control of a flexible manoeuvring system

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

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