Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Selma, Boumediene; Chouraqui, Samira; Abouaïssa, Hassane

doi:10.1007/s41870-020-00436-6

Cited by 15 publications

(7 citation statements)

References 33 publications

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“…Abro et al [34] proposed a model-free based single-dimension fuzzy Sliding Mode Control (MFSDF-SMC) scheme for controlling attitude and position of underactuated quadcopters, and compared it with conventional SMC methods via simulation, results demonstrated that the proposed scheme exhibited robust trajectory tracking performance. In terms of merging fuzzy control and neural networks, a technique known as ANFIS (Adaptive Neuro-Fuzzy Inference System) can be used to solve this problem, which has been used in trajectory tracking of UAVs both as a single control method [35,36] and in combination with PID [37], and is capable of minimizing tracking error, exhibiting greater stability in various flight conditions, and ensuring rapid convergence. Zeghlache et al [38] developed a hybrid technique for the control of coaxial octorotor UAV in the presence of actuator faults that combines fuzzy logic, neural networks, and SMC, and verified that the proposed scheme could significantly reduce the chattering effect and attain good tracking results.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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“…Most researchers [4,5,6,7] have tried to solve the trajectory tracking problems under disturbed conditions. However, they have only shown the computer simulation results in their work, and have not reported the real-time experiment results of a quadcopter.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…However, they have only shown the computer simulation results in their work, and have not reported the real-time experiment results of a quadcopter. A particle swarm optimisation (PSO)-tuned adaptive neurofuzzy inference system (ANFIS) controller is implemented by Selma et al [7] to achieve a robust trajectory tracking scheme for a three-degree freedom quadcopter in a disturbed environment condition. An extensive review article on motion planning of quadcopters or drones and their various control techniques was reported by Elijah et al [8] , and although these authors discussed the physical results associated with the experimental deployment of a quadcopter, they were not elaborately reported in that work.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

“…8 depicts an exploded view of a combined vehicle (UAV + UGV), which indicates all the important components of a vehicle. The UAV's driving assembly includes rotors or blades (1), spider arms (7) and brushless motors (2) for aerial movement. In an aspect, the UAV's driving assembly consists of four brushless motors (2), and motors (2) are attached with the spider arm (7).…”

Section: Brief Description Of the Various Components Of The Uav And I...mentioning

confidence: 99%

“…The UAV's driving assembly includes rotors or blades (1), spider arms (7) and brushless motors (2) for aerial movement. In an aspect, the UAV's driving assembly consists of four brushless motors (2), and motors (2) are attached with the spider arm (7). In a vehicle (UAV + UGV), the UAV's frame also includes a driving circuit ESC ( 14) that is attached to the brushless motors (2).…”

Section: Brief Description Of the Various Components Of The Uav And I...mentioning

confidence: 99%

See 2 more Smart Citations

Design and Analysis of a Novel Concept-Based Unmanned Aerial Vehicle with Ground Traversing Capability

Kumar

Gour

Pandey

et al. 2022

Acta Mechanica Et Automatica

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Unmanned aerial vehicle (UAV) is a typical aircraft that is operated remotely by a human operator or autonomously by an on-board microcontroller. The UAV typically carries offensive ordnance, target designators, sensors or electronic transmitters designed for one or more applications. Such application can be in the field of defence surveillance, border patrol, search, bomb disposals, logistics and so forth. These UAVs are also being used in some other areas, such as medical purposes including for medicine delivery, rescue operations, agricultural applications and so on. However, these UAVs can only fly in the sky, and they cannot travel on the ground for other applications. Therefore, in this paper, we design and present the novel concept-based UAV, which can also travel on the ground and rough terrain as an unmanned ground vehicle (UGV). This means that according to our requirement, we can use this as a quadcopter and caterpillar wheel–based UGV using a single remote control unit. Further, the current study also briefly discusses the two-dimensional (2D) and three-dimensional (3D) SolidWorks models of the novel concept-based combined vehicle (UAV + UGV), together with a physical model of a combined vehicle (UAV + UGV) and its various components. Moreover, the kinematic analysis of a combined vehicle (UAV + UGV) has been studied, and the motion controlling kinematic equations have been derived. Then, the real-time aerial and ground motions and orientations and control-based experimental results of a combined vehicle (UAV + UGV) are presented to demonstrate the robustness and effectiveness of the proposed vehicle.

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Genetic Algorithm Based PID Parameter Optimization for Longitudinal Dynamics of a Fixed Wing Mini UAV

Ulus

Eski

2021

Proceedings of I4SDG Workshop 2021

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Optimal trajectory tracking control of unmanned aerial vehicle using ANFIS-IPSO system

Cited by 15 publications

References 33 publications

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Design and Analysis of a Novel Concept-Based Unmanned Aerial Vehicle with Ground Traversing Capability

Genetic Algorithm Based PID Parameter Optimization for Longitudinal Dynamics of a Fixed Wing Mini UAV

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