Robust Adaptive Flight Controller for UAV Systems

Sarhan, Amr; Qin, Shiyin

doi:10.1109/icisce.2017.252

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…These specifications are for a 2.9 m wingspan and a 30 lb weight. Although the control system of aerosonde is complicated, the ability to perform a mission is highly achievable due to its flexible design [10].…”

Section: The Aerosonde Model 21 System Block Diagram and Descriptionmentioning

confidence: 99%

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

2021

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Synthesis of a flight control system for such an aircraft that achieves stable and acceptable performance across a specified flying envelope in the presence of uncertainties represents an attractive and challenging design problem. This study uses the genetic self-tuning PID algorithm to develop an intelligent flight control system for the aerosonde UAV model. To improve the system's transient responses, the gains of the PID controller are improved using a genetic algorithm (GA). Simulink/MATLAB software is used to model and simulate the proposed system. The proposed PID controller integrated with the GA is compared with the classical one. Three simulation scenarios are carried out. In the first scenario, and at normal conditions, the proposed controller performance is better than the classical one. While in the second scenario, identical results are achieved from both controllers. Finally, in the third scenario, the PID controller with GA shows the robustness and durability of the system compared with the classical PID in presence of external wind disturbance. The simulation results prove the system parameters optimization.

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Section: The Aerosonde Model 21 System Block Diagram and Descriptionmentioning

confidence: 99%

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

2021

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“…One method is robust PID control. For these circumstances, reference [9] developed the robust adaptive PID controller based on UAV fuzzy logic. In reference [10], the UAV's robust response in the presence of uncertainties and disturbances was guaranteed to satisfy the path tracking performance.…”

Section: Related Workmentioning

confidence: 99%

“…A second-order TD is designed, as shown in (9). There are two transition signals ψ 1 (k) and ψ 2 (k).…”

Section: A Tracking Differentiator (Td)mentioning

confidence: 99%

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Sheng

et al. 2021

IEEE Access

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The unmanned aerial vehicle (UAV) is inexpensive and offers a fast response speed and robust flexibility; thus, it is a promising tool in the maritime buoyage inspection scenario, which involves monitoring and accessing a lateral mark system far away from the coast. However, two main problems can occur during inspection. The first is extreme weather conditions, resulting in a deviation between the inspection route and the design route. The second is that the buoyage beacons are visited only once. Therefore, this article proposes a buoyage inspection system consisting of a single UAV and random coastal buoys. The UAV automatically takes off from the depot, performs a self-check on the buoy beacons, and then returns to the depot. A cascade active disturbance rejection controller (ADRC) is designed to adjust the real-time trajectory of the UAV system. A feasible trajectory planning method is also designed based on the continuous Hopfield neural network (CHNN) and genetic algorithm (GA) to minimize the inspection distance. Extensive simulations are conducted to demonstrate the effectiveness of the proposed method.

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“…By using a hybrid scheme of Fuzzy Logic and Neural Networks such as Adaptive Networks, an ANFIS Network harnesses advantages of these two large areas of Computational Intelligence [69]. While Fuzzy Logic can handle uncertainty in inputs and outputs, Adaptive Networks can address the uncertainty of the model and thus, when employed synergistically, can be applied to a wide variety of complex problems [70].…”

Section: The Adaptive-network-based Fuzzy Inference System (Anfis) Almentioning

confidence: 99%

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

Paulino

Guimarães

Shiguemori

2019

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Nowadays, there is a remarkable world trend in employing UAVs and drones for diverse applications. The main reasons are that they may cost fractions of manned aircraft and avoid the exposure of human lives to risks. Nevertheless, they depend on positioning systems that may be vulnerable. Therefore, it is necessary to ensure that these systems are as accurate as possible, aiming to improve navigation. In pursuit of this end, conventional Data Fusion techniques can be employed. However, its computational cost may be prohibitive due to the low payload of some UAVs. This paper proposes a low-cost Multisensor Data Fusion application based on Hybrid Adaptive Computational Intelligence (HACI) -the cascaded use of Fuzzy C-Means Clustering (FCM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS) algorithms -that have been shown able to improve considerably the accuracy of current positioning estimation systems for real-time UAV autonomous navigation, reducing the error in approximately 300cm 2 . In addition, the proposed methodology outperformed two other Computational Intelligence methodologies -Artificial Neural Networks and Regression Models, with 18 different tested approaches -in estimating an UAV position considering the Root-Mean-Square Error against the real trajectory. The generated Fuzzy Inference System has proved to be effective in providing an improved positioning estimation with a low computational burden, about 600 times faster than the fastest embedded sensor refresh rate.

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Robust Adaptive Flight Controller for UAV Systems

Cited by 18 publications

References 20 publications

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Smart aerosonde UAV longitudinal flight control system based on genetic algorithm

Maritime Buoyage Inspection System Based on an Unmanned Aerial Vehicle and Active Disturbance Rejection Control

Hybrid Adaptive Computational Intelligence-based Multisensor Data Fusion applied to real-time UAV autonomous navigation

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