Robust Control Algorithm for Drones

Priya, P. Lakshmi; Kamlu, Sushma S.

doi:10.5772/intechopen.105966

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…The Proportional Integral Derivative (PID) controller is the aviation industry's most widely used control algorithm. It is a linear controller with the advantages of being easy to tune and alter parameter gains, simple to construct, and strong resilience [19]. Authors in reference [20] by linearizing the quadrotor model and decoupling the state, designed a PID controller to stabilize and control the quadrotor.…”

Section: A Related Workmentioning

confidence: 99%

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Madebo,

Abdissa,

Lemma

et al. 2024

IEEE Access

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In this paper, an intelligent Model Reference Adaptive Control (MRAC) based on a neural network is proposed for robust tracking control of quadrotor UAV under external disturbances and parameter variations. First, the singularity-free dynamic model of the quadrotor is developed using Newton-Quaternion formalism. Then, conventional MRAC is designed to generate training data. With the generated data, the Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) are trained offline to get an initial set of network parameters for position controller parameters estimation and attitude control of the quadrotor, respectively, and an online learning algorithm is developed to update those network parameters in real-time. Finally, the performance of the designed Neural network-based MRAC has been evaluated using a numerical simulation in a nominal scenario and by introducing parametric variation and external disturbances as matched and unmatched uncertainties into the system. The simulation results show that the proposed controller has a better tracking performance and disturbance rejection capability compared with the Linear Quadratic Regulator (LQR) and conventional MRAC. Furthermore, the utilized control efforts are minimal and smooth proving functional safety and economical use of the controller. Therefore, the suggested controller is feasible for real-time implementation of the quadrotor UAV.

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Section: A Related Workmentioning

confidence: 99%

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Madebo,

Abdissa,

Lemma

et al. 2024

IEEE Access

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“…There are several ways to classify UAV control methodologies [94,203,204], based on factors such as the type of UAV being controlled, the control objectives, and the mathematical techniques employed. Here are some common classifications in control theory: feedback control vs. feedforward control; linear control vs. nonlinear control [205]; continuous-time control vs. discrete-time control; deterministic control vs. stochastic control; model-based control vs. model-free control, robust control [206,207], and adaptive control [208,209]; centralized control vs. decentralized control; optimal control vs. suboptimal control; and time-invariant control vs. time-varying control. These classifications represent the different approaches and algorithms used in flight dynamics control.…”

Section: Advancements In Aircraft Control: An Overview Of the Develop...mentioning

confidence: 99%

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

Telli,

Kraa,

Himeur

et al. 2023

Systems

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The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

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“…Because of its nonlinear characteristics and underactuated design, UAVs seem to be an excellent platform to control systems research [118]. Control models pertain to the control of dynamic systems to drive them to a desired state while minimizing delays, the occurrence of a signal or function exceeding its target (overshooting) or steady-state error, and securing control stability [119].…”

Section: Control Modelsmentioning

confidence: 99%

Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

Aretoulaki

Ponis

Plakas

2023

Drones

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The adoption of drones and other emerging digital technologies (DTs) has proven essential in revolutionizing humanitarian logistics as standalone solutions. However, the interoperability of humanitarian drones with other DTs has not yet been explored. In this study, we performed a systematic literature review to attempt to fill this gap by evaluating 101 mathematical models collected from Scopus. After conducting a descriptive analysis to identify the trends of publications in terms of year, type, source, and country of origin, a content analysis ensued to investigate the complementarity, interoperability, and level of integration of humanitarian drones with eight DTs. Next, we researched how these DTs can help drones exploit their capabilities to their full potential and facilitate the various drone operations deployed across different disaster scenarios, types, and stages. Last, the solving approaches employed by the models were examined. Overall, we shifted our research focus toward several overlooked aspects in the literature and identified multiple challenges needing to be addressed. Our work resulted in the formulation of a holistic framework aiming to standardize the cooperative utilization of DTs during the execution of humanitarian drone operations, so as to enhance their real-life application and scalability.

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Robust Control Algorithm for Drones

Cited by 5 publications

References 38 publications

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

Robust Tracking Control for Quadrotor UAV With External Disturbances and Uncertainties Using Neural Network Based MRAC

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

Complementarity, Interoperability, and Level of Integration of Humanitarian Drones with Emerging Digital Technologies: A State-of-the-Art Systematic Literature Review of Mathematical Models

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