From Control Requirements to PIL Test: Development of a Structure to Autopilot Implementation

Santos, Marcelo Henrique dos; Oliveira, Neusa Maria Franco; d’Amore, Roberto

doi:10.1109/access.2021.3127846

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Likewise, Ulker et al [129] conducted PILS for a fixed-wing UAV in windy conditions in various flight scenarios, including straight-and-level flight, level climb, and turn. Similarly, authors of [130] used PILS to validate their flight control algorithm and microcontroller's performance. Hardware-in-the-loop simulation comes after PILS has been effectively implemented.…”

Section: Realistic Simulation Techniquesmentioning

confidence: 99%

Control Algorithms, Kalman Estimation and Near Actual Simulation for UAVs: State of Art Perspective

Tahir

Mir

Islam

2023

Drones

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The pervasive use of unmanned aerial vehicles for both commercial and military operations has undergone rapid development in the recent past. When designing unmanned aerial vehicles, it is highly desirable for them to be able to complete their missions with minimal human intervention. Reaching full autonomy requires a reliable and efficient control algorithm that can handle all flight conditions. Due to the confidential nature of UAV design and development, there is a lack of comprehensive literature on the subject. When it comes to the practical application of the ideas presented in the literature, the situation is even bleaker. This research not only examines the flight phases in which controllers and estimators are used for UAVs but also provides an in-depth analysis of the most recent and state-of-the-art control and estimate techniques for UAVs. Research opportunities and challenges specific to UAVs were also examined in this study in an effort to raise the bar for UAV design as a whole and smooth the way for researchers to go from simulation-based research to practical applications. This review paper establishes a foundation that not only investigates the inherent flight dynamics, control architecture, and Kalman estimators utilized in the development of UAVs but also points out the shortcomings that currently exist in research. A number of design considerations for realistic applications and potential studies are presented in the conclusion.

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Section: Realistic Simulation Techniquesmentioning

confidence: 99%

Control Algorithms, Kalman Estimation and Near Actual Simulation for UAVs: State of Art Perspective

Tahir

Mir

Islam

2023

Drones

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A Review of UAV Platforms for Autonomous Applications: Comprehensive Analysis and Future Directions

Tahir¹,

Mir²,

Islam

2023

IEEE Access

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Unmanned aerial vehicles have evolved rapidly in the recent past for their widespread utilization for both commercial and military purposes. It is extremely desirable while designing unmanned aerial vehicles, to enable them to accomplish their tasks with little human intervention. To achieve full autonomy, an optimal and robust control algorithm that covers complete flight phases is needed. Being propriety in nature, very little information is available in the literature that encompasses all modalities related to UAV design and development. Literature is even more dreath when it comes to near-actual implementation of the developed strategies. This study provides a comprehensive and in-depth review of the most recent and state-of-the-art control and estimation techniques for UAVs, as well as an examination of the flight phases in which those controllers and estimators are utilized. This study also suggests UAV-related research that would improve the overall quality of UAV design and facilitate the transition from software simulations to hardware implementation. Through this paper, a comprehensive platform is established that not only examines the controller and state estimators used for the development of autonomous UAVs but also identifies and discusses the limitations of existing research. In conclusion, several practical implementation considerations and future research directions are proposed.INDEX TERMS Autonomous missions, fixed wing, flight controller, landing, state estimation, take-off, unmanned aerial vehicle.

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Machine Learning and Deep Learning for Intelligent Systems in Small Aircraft Applications

Rahamathunnisa,

Mohanty,

Sudhakar

et al. 2023

Handbook of Research on AI and ML for Intelligent Machines and Systems

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This chapter explores the integration of machine learning and deep learning techniques in small aircraft applications. The aviation industry is exploring innovative solutions to improve safety, efficiency, and performance in these operations. The chapter explores the advantages, challenges, and future prospects of implementing intelligent systems in small aircraft, including autopilot systems, navigation assistance, fault detection, and pilot support systems. Real-world case studies and applications demonstrate the transformative impact of these technologies on small aircraft operations. The chapter provides a comprehensive overview of the latest advancements in machine learning and deep learning, highlighting their pivotal role in improving small aircraft intelligence, safety, and efficiency.

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From Control Requirements to PIL Test: Development of a Structure to Autopilot Implementation

Cited by 3 publications

References 33 publications

Control Algorithms, Kalman Estimation and Near Actual Simulation for UAVs: State of Art Perspective

Control Algorithms, Kalman Estimation and Near Actual Simulation for UAVs: State of Art Perspective

A Review of UAV Platforms for Autonomous Applications: Comprehensive Analysis and Future Directions

Machine Learning and Deep Learning for Intelligent Systems in Small Aircraft Applications

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