International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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A Review on Drone Technology and Control Systems Honmore Pooja D.1, Peerzade Needa S, 2, Shaikh Md. Sameer S.3 Harage Shrikrant A.4 2,3 Diploma Student, Electronics & Computer Engg. Sanjay Bhokare Group of Institutes, Miraj, Maharashtra, India. 1 Professor, Electronics & Computer Engg. Sanjay Bhokare Group of Institutes, Sangli, Maharashtra, India.
⁴HOD, Electronics & Computer Engg, Sanjay Bhokare Group of Institutes, Miraj, Maharashtra, India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Drone technology, also known as Unmanned
dynamic environmental conditions [17], [20]. This review systematically examines existing literature to understand the evolution of drone technology and control mechanisms.
Aerial Vehicle (UAV) technology, has shown rapid growth in recent years due to its wide range of applications in both civilian and industrial domains. Initially developed for military operations, drones are now extensively used in agriculture, surveillance, logistics, healthcare, construction, and disaster management. This systematic literature review presents a structured analysis of recent research related to drone technology and control systems. The review focuses on drone architectures, control mechanisms, sensor integration, artificial intelligence-based autonomy, and emerging trends such as swarm intelligence. Various research papers published in reputed journals and conferences are analyzed to identify technological advancements, application areas, challenges, and research gaps. The study highlights how modern control systems, combined with machine learning and artificial intelligence, have improved flight stability, navigation accuracy, obstacle avoidance, and decision-making capabilities of drones. However, issues related to battery life, security, regulation, and reliable autonomous control still remain open challenges. This review aims to provide a clear understanding of the current state of drone control systems and offers direction for future research in autonomous and intelligent UAV systems.
2. DRONE ARCHITECTURE AND CLASSIFICATION Drone systems consist of multiple hardware and software components that work together to achieve stable and controlled flight. The basic architecture includes the airframe, propulsion system, sensors, flight controller, communication modules, and payload [5], [6]. Drones are commonly classified based on their design and operation:
Each configuration requires different control strategies to manage lift, thrust, and stability effectively.
Key Words: Drone Technology, UAV, Control Systems, Autonomous Navigation, Artificial Intelligence, Machine Learning, Swarm Intelligence
3. DRONE CONTROL SYSTEMS 3.1 Flight Control Mechanisms
1. INTRODUCTION
Flight control systems are responsible for stabilizing the drone and executing pilot or autonomous commands. Traditional control approaches include Proportional– Integral–Derivative (PID) controllers, which are widely used due to their simplicity and effectiveness [17]. However, PID controllers struggle in highly dynamic or uncertain environments.
Drones commonly referred to as Unmanned Aerial Vehicles (UAVs), are aircraft systems that operate without an onboard human pilot. Initially developed for military surveillance and defence missions, drones are now widely used in civilian sectors such as agriculture, logistics, healthcare, construction, and environmental monitoring [1], [3], [4]. The rapid growth of drone applications is driven by advancements in lightweight materials, sensor technology, embedded systems, and intelligent control algorithms [5], [6].
Recent research focuses on adaptive and intelligent control algorithms that can handle communication delays, sensor noise, and incomplete information [20]. These methods improve robustness and safety during autonomous flight.
Modern drones are no longer limited to manual or remote operation. The integration of artificial intelligence (AI) and machine learning (ML) has enabled autonomous navigation, obstacle avoidance, and intelligent decision-making [2], [13], [14]. Control systems play a critical role in ensuring flight stability, trajectory tracking, and safe operation under
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Fixed-wing drones, suitable for long-range missions with higher endurance. Rotary-wing drones (multi rotor/copters), preferred for vertical take-off, hovering, and precise maneuvering [17]. Hybrid drones, combining features of both fixedwing and rotary-wing platforms [19].
3.2 Artificial Intelligence in Drone Control AI-based control systems allow drones to perceive their environment and make decisions without human intervention. Machine learning and deep learning techniques are used for path planning, object detection, and collision
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