Nomenclature
- GPS
-
Global Positioning System
- UAV
-
Unmanned aerial vehicle
- GLONASS
-
Global Navigation Satellite System
1.0 Introduction
Examining adaptive algorithms for drone flight control in scenarios of restricted communication and partial information is both pertinent and essential given the escalating deployment of drones across diverse sectors, including commercial, governmental, and military applications. With the increasing probability of communication failures and unforeseen situations, effective drone control becomes a key factor to ensure the safety and efficiency of drone operations. Research on this topic will enable the development of algorithms capable of adapting to changing conditions and making real-time decisions, which will increase the reliability of autonomous control systems and improve their ability to function in dynamic and unpredictable environments. This in turn contributes to the development of safe and efficient autonomous flight technologies with a wide range of practical applications ranging from delivery and monitoring to rescue and public safety.
The research challenge lies in the difficulty of developing adaptive algorithms to control drone flight under conditions of limited communication and incomplete information, due to the dynamism of the environment and the diversity of possible scenarios, which requires finding effective solutions to ensure the safety and efficiency of drone operations in different situations. The main challenges include the need for algorithms that can handle and adapt to variable environmental conditions, make real-time decisions, and ensure flight safety even when communication with the base station is lost or access to information about the environment is limited. It is also important to consider various technical and organisational constraints, such as limitations of computing resources on board the drone and data privacy requirements. Addressing these challenges will enable more robust and adaptive control systems, which is a key factor for the successful development of autonomous drone flight in various applications.
According to the study by Telli et al. [Reference Telli, Kraa, Himeur, Ouamane, Boumehraz, Atalla and Mansoor1], adaptive drone flight control algorithms represent a key aspect to ensure the reliability and safety of autonomous flights under variable environmental factors. However, the study does not address a deeper understanding of the impact of environmental variables, such as weather conditions and obstacles, on the performance of adaptive drone flight control algorithms. The work of Quamar et al. [Reference Quamar, Al-Ramadan, Khan, Shafiullah and El Ferik2] emphasises the importance of integrating satellite navigation and environmental estimation algorithms for effective drone performance under conditions of incomplete information. The work does not focus on analysing the potential risks and threats associated with the use of adaptive control algorithms, including possible accident scenarios and methods to prevent them. A study by Guo et al. [Reference Guo, Ye, Liu and Peng3] found that adaptive algorithms based on statistical methods and feedback show better performance and reliability in drone flight control compared to traditional methods. The study does not consider how to optimise the use of limited resources on board the drones to implement adaptive control algorithms, such as computational power and energy.
Following Lee et al. [Reference Lee, Chuang, Kuo and Chen4], one of the main challenges is to develop algorithms that can adapt to rapidly changing flight conditions and provide smooth and safe drone movement in real time. The researchers do not pay attention to the issues of interaction and cooperation between drones when applying adaptive control algorithms under communication constraints and incomplete information. The study, conducted by Qu et al. [Reference Qu, Calyam, Yu, Vandanapu, Opeoluwa, Gao, Wang, Chastain and Palaniappan5], points out the need to consider computational resource constraints and network latency when designing control algorithms to ensure their effectiveness and applicability in practice. However, the work does not consider the effectiveness and safety of autonomous drone flights in real-world environments when applying the developed adaptive control algorithms. The work of Hussein et al. [Reference Hussein, Nouacer, Corradi, Ouhammou, Villar, Tieri and Castiñeira6] emphasises the importance of further research in the field of adaptive control of drone flight in order to develop new methods and technologies to ensure their safe and efficient operation in various operating conditions. But the study does not consider the issues associated with the application of adaptive drone control algorithms, including privacy and liability issues for possible accidents or incidents.
The research challenge lies in the difficulty of developing adaptive algorithms to control drone flight under conditions of limited communication and incomplete information, due to the dynamism of the environment and the diversity of possible scenarios. This necessitates coming up with practical ways to guarantee the effectiveness and safety of drone operations in various circumstances. The primary obstacles include the requirement for algorithms that can make judgements in real time, manage and adjust to changing environmental circumstances, and guarantee flight safety even in the event of a communication failure or restricted access to environmental data. It’s also critical to take into account a variety of organisational and technical restrictions, such as the drone’s limited computer capacity and data protection laws. In order to successfully create autonomous drone flight in a variety of applications, it will be necessary to address these problems in order to enable more resilient and adaptable control systems.
The aim of this research is to develop adaptive drone flight control algorithms that can successfully operate in situations of limited communication and insufficient information. The main objectives of the study include addressing the key challenges associated with developing control algorithms, making real-time decisions without relying on continuous communication with a base station, and ensuring flight safety even when access to critical information about the drone’s surroundings is limited.
2.0 Materials and methods
The study was conducted at the National Aviation University, in particular, at the Aeronautics Training and Research Institute, which is part of the Faculty of Aeronautics, Electronics, and Telecommunications. Specifically, the study was carried out with the participation of specialists and researchers from the Department of Aeronautical Systems. The study considered a specific geographical feature related to the location of Xi’an city. This geographical setting involves features of climate, local topography and architectural surroundings that can have a significant impact on the flight conditions for drones. Xi’an city, as a large metropolitan area, is characterised by high built-up areas and the presence of many buildings and other architectural features, which creates special conditions for navigation and communication between drones and operators. The developed algorithms were tested in a series of DJI Flight Simulator simulations, which were conducted under conditions of limited communication and incomplete information. The use of the Matlab Simulink software package allowed the simulation and analysis of drone behaviour under various conditions, including atmospheric conditions and interference. This software tool provided a convenient means to create models of control and planning systems and to verify their performance in real time. It also allowed algorithms to be tested and optimised before they were actually applied to the drones. The first step was to define the main components of the system and their interactions, which allowed creating an abstract representation of how the system works. The individual blocks responsible for different aspects of the system, including route planning, motion control and response to external influences, were then developed and customised. The performance and effectiveness of the system was then tested in various scenarios using simulation modelling under different conditions such as weather changes, obstacles and other factors.
Using computer modelling and simulation techniques, a virtual space was created that adequately reflected the real-world conditions in which the drones operate. This allowed experimentation and testing of the control algorithms in different scenarios such as changing weather conditions, obstacles, communication limitations. By modelling different situations and changing parameters, the effectiveness, and reliability of the control algorithms under different conditions were evaluated. In addition, computer modelling and simulation significantly reduced the time and cost normally required to conduct real experiments on real drones. The analytical method allowed a detailed study of various aspects of drone flight, including its motion, dynamics, control and interaction with the environment. Using this method, key flight characteristics such as velocity, acceleration, pitch angles and factors affecting the stability and manoeuvrability of the drone were identified. This included analysing the forces acting on the drone during flight, such as lift, drag and thrust, as well as the impact of environmental conditions like wind and turbulence. The analytical method allowed the identification of important parameters to be considered when developing adaptive control algorithms, such as adequate response to changes in external conditions, risk minimisation and flight safety.
The statistical method was used to study large amounts of data collected during experiments and observations of drone flights. This method allowed the identification of patterns and trends in drone behaviour under different conditions, as well as the assessment of the degree of variation and risks. The researchers collected data on factors like flight duration, distance covered, deviations from the planned trajectory and environmental variables such as wind speed and direction. The statistical method provided a deeper understanding of drone behaviour and helped in the development of adaptive control algorithms to be more responsive to the stochastic nature of the operating environment. By applying the functional method, various functions and properties that characterise the performance of drones and their control systems were investigated. This method allowed for identifying the main functional blocks and subsystems of drones, such as navigation, motor control, stabilisation systems. The study of functional relationships between these blocks and the evaluation of their influence on the overall system performance became the basis for the development of adaptive control algorithms. For example, the researchers examined how changes in the navigation subsystem impacted the drone’s ability to maintain a stable flight path, and how the motor control system responded to disturbances. This provided insights into the interdependencies within the drone system and guided the design of the adaptive control architecture.
The deduction method helped in identifying the basic regularities and principles of adaptive drone flight control algorithms based on known facts and data. By studying the available knowledge about flight dynamics, aerodynamics, technical characteristics of drones and the influence of various factors on their operation, the deduction method allowed deriving general principles and laws that formed the basis for the development of adaptive algorithms. This involved knowing how to apply the basic ideas of control theory, such as feedforward, feedback and adaptation, to the control of drone flight. The researchers’ use of a deductive approach enabled them to expand on previously acquired knowledge in the fields of control engineering and aviation to create cutting-edge adaptive techniques specifically designed to meet the unique demands of drone operations. With the help of the description method, the main characteristics and features of drone operation and the principles of their functioning in different conditions were highlighted. This method made it possible to create a detailed description of flight control processes, including drone interaction with the environment, reaction to changing parameters and decision-making based on available information. The researchers documented the various sensors, actuators and computational components of the drone system, as well as the algorithms used for tasks like trajectory planning, obstacle avoidance and communication management.
3.0 Results
Adaptive algorithms for drone flight control are computational methods that adjust a drone’s control system in real time based on changing environmental conditions and operational parameters. These algorithms allow drones to dynamically respond to factors such as wind, obstacles or varying payloads by modifying flight behaviours to maintain stability and efficiency. Adaptive algorithms for drone flight control under communication constraints and incomplete information are crucial to ensure the safety and efficiency of autonomous flights. Adaptive drone flight control algorithms represent a key element in ensuring efficient and safe drone operations under conditions of limited communication and insufficient information. Such situations require the development of intelligent systems capable of adapting to variable environmental conditions and making decisions based on limited data. Some key principles and technologies that can be utilised in adaptive drone control algorithms in such scenarios (Fig. 1).
Adaptive drone flight control algorithms that can successfully operate in situations of limited communication and insufficient information. Source: compiled by the author.
The use of machine learning techniques such as neural networks or reinforcement learning algorithms allows drones to adapt to changing conditions and make decisions based on experience. These algorithms can learn from existing flight data as well as in real time using sensor information and external data [Reference Kondratenko, Klymenko and Sidenko7]. The development of algorithms that allow drones to make decisions independently based on localised information reduces dependence on a centralised controller and allows the drones to continue performing tasks even if communication with the base station is lost. The use of probabilistic methods, such as Kalman filters or particle filters, allows the drone to estimate the state of the environment and adjust the drone’s actions based on this estimate. This allows drones to adapt to variable conditions and maintain flight stability even with limited information.
Developing reinforcement learning or teacher learning algorithms allows drones to improve their control skills based on experience and sharing information with each other. This also allows drones to adapt to new situations and make effective decisions even with limited communication with the base station or operator. The implementation of redundancy and autonomous decision-making systems allows drones to remain operational and continue to perform missions even in the event of communication failures or incomplete information [Reference Hysa8]. This ensures mission continuity and enhances drone reliability in a communications-constrained environment. In general, the successful operation of drones in situations of limited communication and insufficient information requires a set of adaptive control algorithms capable of adapting to variable conditions and making effective decisions based on limited data. This enables drones to perform their tasks efficiently and ensures their reliability and safety in different usage scenarios.
The development of an autonomous control system with route planning in the MATLAB/Simulink software package began with the development of a system model, which is a sequence of individual blocks combined to provide optimal control. This approach allowed the creation of an autonomous control system capable of adapting to different situations and ensuring optimal fulfilment of tasks (Fig. 2).
General control system that is immune to external interference.
An important aspect of such a control system is the ability to adapt to variable conditions and maintain stable operation even in the presence of unfavourable external influences. This is achieved by applying various techniques and methods such as data filtering, noise compensation, prediction of changes and correction of control signals. Simulation modelling under different conditions such as weather changes, obstacles and other factors have been used to test the system performance and efficiency in different scenarios. Simulations of different weather conditions such as strong wind, rain or fog allowed the evaluation of how the control system reacted to such changes. The results showed how efficiently and reliably the drone adapted to the changing atmospheric conditions and continued to fulfil its tasks. In the case of high winds, the control system automatically adjusted the flight path and controlled the drone’s motors to compensate for wind-induced drift. For rainy weather, the system took into account moisture and water that could get on the drone’s cameras or sensors and took steps to ensure that the electronics were clean and working properly. In the case of fog, the system relied on other sensors and location data such as Global Positioning System (GPS) to provide a safe and accurate navigation solution.
When compared to conventional drone control techniques, the simulation results showed that the adaptive control algorithms performed noticeably better. Important performance indicators, including trajectory tracking precision, flight stability and mission completion rate, demonstrated significant improvements in the evaluated environmental conditions with limited communication and insufficient information. In the presence of high winds and obstructions, for instance, the adaptive algorithms were able to maintain an average trajectory variation of less than 2m, but in similar conditions, the classic control methods showed deviations of more than 5m. Furthermore, the adaptive algorithms’ mission completion percentage was above 90%, whereas the older methods’ rate was just 75%.
During the simulation, obstacles such as buildings, trees or other objects that could be encountered in the drone’s path were introduced. The control system had to correctly detect these obstacles, choose safe avoidance routes, and continue with the mission. Simulation results demonstrated the effectiveness of the route planning and obstacle avoidance algorithms. When obstacles were detected, the control system automatically calculated optimal avoidance routes, considering their size, shape and other characteristics. This allowed the drone to avoid collisions and maintain a safe distance from obstacles. The simulations also considered other factors such as communication limitations, unexpected changes in the environment or the behaviour of other drones in the general flight area. This approach allowed assessing the level of adaptability of the control system and its ability to make appropriate decisions in real time to ensure safe and efficient flight. Setting up the simulations in this way allowed assessing how the control system responded to different scenarios and conditions that could occur during flight. For example, if communication with the operator or other drones was lost, the system had to be able to decide on further actions based on available information and pre-defined parameters.
During the simulations, the research team encountered several key challenges that had to be addressed. Accurately simulating the dynamic environmental conditions, such as changing wind speeds, precipitation, fog and the existence of impediments like buildings, was one of the biggest problems. In order to solve this, the researchers integrated sensor data and created intricate models of atmospheric impacts to give the drone’s control system real-time environmental information. Making sure the control algorithms could adjust to changing circumstances fast enough to prevent instability was another difficulty. In order to strike a balance between responsiveness and stability, the team experimented with various feedback systems and control law tweaking. They also had to optimise the algorithms to perform well on the on-board processors, taking into consideration the drone’s limited computational resources. The researchers created adaptive control techniques that could successfully manage the dynamic and resource-constrained environment simulated in MATLAB/Simulink through an iterative process of simulation, analysis and refining.
The results of the simulations allowed the performance and effectiveness of the control system to be assessed under different conditions and helped identify potential problems or improvements that could be made to the system. This helped to ensure a high level of reliability and performance of the drone systems in real-world environments. Prediction plays a key role in modern drone control, especially in a variable and unpredictable environment [Reference Radzki, Nielsen, Golińska-Dawson, Bocewicz and Banaszak9]. The use of machine learning and data analysis techniques opens the door for drones to adapt to various changes, which significantly improves their efficiency and safety. Prediction allows drones to react quickly to changes in the environment. Machine learning techniques can analyse large amounts of data, such as data from drone sensors and external weather or traffic data, and use it to make predictions about potential changes [Reference Falko, Gogota, Yermolenko and Kadenko10]. For example, drones can predict weather changes to avoid flying in unfavourable conditions, or predict traffic flows to choose the best route. Prediction helps drones adapt to limited information. In environments where complete information about the environment is not always available, data analysis techniques allow drones to fill in information gaps and make assumptions based on available data. For example, drones can predict object trajectories or weather changes based on analysing historical data.
Decentralised algorithms allow drones to make decisions more flexibly and quickly. Instead of relying on a centralised controller, each drone can analyse local information, such as sensor data and the results of its actions, and make decisions based on this information. This allows drones to quickly adapt to changing conditions and respond effectively to unforeseen situations. Decentralised algorithms reduce the risk of the entire system failing due to the failure of a central node. Since each drone makes decisions independently of the others, the failure or loss of communication of one drone does not disrupt the other drones. This increases the reliability and resilience of the system as a whole. Decentralised algorithms allow drones to better adapt to communication constraints. In situations where there is limited or no communication between drones or with the central controller, decentralised algorithms allow drones to continue to perform their tasks based on available local information.
Feedback and trajectory correction play an important role in providing reliable and safe drone control [Reference Yang, Zhou and You11]. This process involves using feedback from sensors on board the drone to continuously monitor its position, motion and environment. Based on this information, the drone then adjusts its flight path in real time to ensure reliable and accurate task execution even under unfavourable conditions. The use of feedback allows the drone to receive continuous updates on its current position and environment. This includes data on altitude, speed, tilt angles and other parameters collected from inertial sensors, GPS and other sensors. By receiving this data, the drone can estimate its position relative to the target trajectory and make necessary adjustments. Feedback allows the drone to respond to changes in the environment and unforeseen situations. For example, if the drone encounters strong winds or a sudden change in weather conditions, feedback will help it adapt to the new conditions and maintain stable flight. This is especially important in the event of a loss of communication with the base station or operator, when the drone must be able to make decisions on its own to ensure a safe return or mission completion. Feedback and trajectory correction ensure the drone’s reliability and stability in a variety of operating conditions. Even in the face of temporary communication interference or environmental changes, the drone retains the ability to accurately control and execute missions, enhancing its effectiveness and safety in a wide range of usage scenarios.
The use of probabilistic methods such as Kalman filters or particle filters provide powerful tools for estimating the state of the environment and correcting drone actions based on this estimation [Reference Zitar, Mohsen, Seghrouchni, Barbaresco and Al-Dmour12]. Probabilistic methods allow estimating the state of the environment, taking into account different sources of uncertainty. Drones equipped with sensors collect data about their position, surrounding objects and flight conditions. However, this data often contains noise and measurement errors. Probabilistic methods can effectively filter out such noise and provide accurate estimates of the environment even in the presence of inaccurate data. Probabilistic methods allow drones to make decisions based on statistical models of the environment. Kalman filters, for example, can predict the future state of an object based on its current position and velocity, while accounting for random changes and noise in the data. This allows drones to make informed decisions and respond to changing conditions with a high degree of accuracy and reliability. Probabilistic methods provide the ability to adapt to different scenarios and flight conditions. Particle filters, for example, use a Monte Carlo (a statistical technique that uses repeated random sampling to obtain numerical results and solve problems that might be difficult to solve analytically) method to efficiently update system state estimates based on new data. This allows drones to adapt to unexpected changes and dynamically changing conditions in real time. The use of probabilistic methods contributes to the safety and reliability of drones. By incorporating uncertainty and probability distributions into environmental estimates, drones can avoid collisions with obstacles, navigate in limited visibility conditions, and effectively ensure safe flight for themselves, surrounding objects and people.
Distributed algorithms allow drones to improve their control skills based on their own experience and sharing information with other drones [Reference Venturini, Mason, Pase, Chiariotti, Testolin, Zanella and Zorzi13]. Distributed learning algorithms facilitate continuous improvement of drone control skills. Reinforcement learning algorithms, such as Q-learning or deep learning methods, allow drones to adapt to different scenarios and environments by systematically learning the results of their actions and adjusting their behavioural strategy based on their experience. This allows drones to effectively adapt to changing environments and improve their performance characteristics over time. Distributed learning algorithms facilitate the sharing of knowledge and experience between drones. By sharing their experiences and learning outcomes, drones can learn from each other and adapt their behaviour based on the collective experience. This collective learning approach allows drones to reach optimal control strategies faster and more efficiently, and improves the overall performance level of the system. Distributed learning algorithms contribute to the flexibility and adaptability of drones in different application scenarios. Learning with a teacher allows drones to quickly learn new knowledge and skills based on feedback from human operators or other data sources. This allows drones to efficiently adapt to new tasks and conditions, which is especially important in dynamic and unpredictable environments. Distributed learning algorithms contribute to the autonomy and self-reliance of drones. Trained drones can make decisions and perform tasks without constant human intervention, increasing their efficiency and real-time performance. This is especially important in situations where there is limited or no communication with the operator.
Implementing redundancy systems allows drones to remain operational even in the event of failures in core systems or loss of communication with the operator. Automatic redundancy systems, such as power or control redundancy, can provide the drone with sufficient time to take action to prevent emergencies or return to a safety base. Autonomous decision-making systems allow drones to autonomously analyse the situation and take the necessary actions to ensure safe flight [Reference Quinones-Grueiro, Biswas, Ahmed, Darrah and Kulkarni14]. For example, autonomous control systems can switch to alternative routes, avoiding dangerous areas or obstacles, or even automatically return to base if malfunctions are detected. The introduction of redundancy and autonomous systems allows drones to maintain mission continuity even in the face of limited connectivity or incomplete information. Drones can continue their missions by relying on localised data and decision-making algorithms, increasing their efficiency and autonomy in real time.
Redundancy and autonomy contribute to increased trust and acceptance of drones as reliable tools in various applications. By enabling autonomous decision-making and safety in case of contingencies, drones are becoming more attractive for commercial use as well as security, medical, search and rescue and other applications. Performance evaluation methods for complex systems are powerful tools to evaluate the performance, safety and functionality of autonomous systems including drones and other unmanned devices [Reference Alam and Oluoch15, Reference Yermolenko, Klekots and Gogota16]. Performance evaluation methods provide a clear definition of the criteria to be considered in the design and use of such systems. This includes both technical parameters and parameters related to the safety of both the device itself and the environment. Such analysis helps engineers and developers to focus on key aspects, ensuring optimal performance and minimising risks. Evaluation methods provide the ability to analyse test results and verify the performance of control algorithms. By comparing actual data against predefined criteria, engineers can evaluate the performance and reliability of the system under different conditions. This identifies possible bottlenecks and problems that may arise during operation. Evaluation methods serve as a tool to identify and analyse weaknesses in the system and suggest ways to improve them. Conducting systematic analyses enables the identification of vulnerabilities and weaknesses and the development and implementation of appropriate remedial measures. This contributes to improving the reliability, safety and efficiency of autonomous systems as a whole. Performance evaluation methods for complex systems represent an integral part of the engineering process, ensuring quality control and continuous improvement of technologies, including the development and use of autonomous systems such as drones.
Unmanned aerial vehicle (UAV) flight control plays an important role in the modern aviation industry by providing the basic principles and methods required for effective flight control [Reference Zuo, Liu, Han and Song17]. This aspect of aviation technology has a significant impact on the development and utilisation of drones, enhancing their applications in various fields. UAV flight control provides basic principles that can be adapted for use with drones. These principles include an understanding of the basic principles of aviation, aerodynamics and flight mechanics, which is fundamental to the development of effective drone control algorithms. Meanwhile, basic control principles and techniques developed for UAVs can be successfully adapted to drone control, given their specific characteristics and capabilities. UAV flight control involves various techniques such as autopiloting, navigation and flight control, which are key components for the development of adaptive drone control algorithms. Autopiloting, for example, allows certain aspects of flight control to be automated, which improves the accuracy and stability of drone flight. Navigation techniques enable the location and direction of drones to be determined, which has a significant impact on their efficiency and safety. Flight control provides the ability to regulate drone motion and behaviour in real time, which is an important aspect in the development of adaptive control algorithms [Reference Babak, Babak, Eremenko, Kuts, Myslovych, Scherbak and Zaporozhets18, Reference Kharlamov, Krivtsun, Korzhyk, Ryabovolyk and Demyanov19]. UAV flight control represents a fundamental element for the development and utilisation of drones. The basic principles and methods, as well as the autopiloting, navigation and flight control techniques provided by this aspect of aviation technology, are the basis for the development of adaptive drone control algorithms that are able to function effectively in a variety of environments and ensure the safety and reliability of autonomous flight.
Significant advantages in terms of environmental effect and energy efficiency were also shown by the application of adaptive control techniques. The adaptive system minimised needless energy expenditure and optimised power usage by enabling the drones to dynamically modify their flight settings in response to changing conditions. For instance, the algorithms could recognise opportunities to glide or coast instead of sustaining constant effort, or they could recognise tailwinds and modify thrust accordingly. Comparing this to conventional control methods resulted in longer flight periods and less battery consumption. Furthermore, by avoiding unnecessary manoeuvres and navigating around obstructions, the adaptive algorithms helped reduce noise, pollution and other environmental disruptions brought on by the drone operations.
Satellite communications and navigation play a key role in the development and control of drones, providing information on location, direction and other parameters required for the effective operation of these systems [Reference Nguyen, Rohacs and Rohacs20, Reference Biliuk, Shareyko, Savchenko, Havrylov, Mardziavko and Fomenko21]. This aspect of technology provides a wide range of capabilities and limitations that need to be considered when developing control algorithms. Satellite communications and navigation provide information on various navigation technologies including GPS, GLONASS and other systems. These technologies provide highly accurate and reliable real-time positioning of drones, which is a key aspect for successfully controlling their flight. Satellite communications and navigation allow the capabilities and limitations of these technologies to be considered when developing drone control algorithms. For example, some areas may have limited access to satellite signal due to geographical features or atmospheric conditions, which can affect the accuracy and reliability of drone navigation. Satellite communications and navigation may be useful for implementing positioning and control algorithms in environments with limited satellite signal availability [Reference Azarov, Kolesnyk and Krupelnitskyi22]. For example, additional sensors or correction algorithms may be included in the drone control system to compensate for loss of satellite communication and provide reliable positioning and control of the drone. Satellite communication and navigation play an important role in the development of drone control algorithms by providing essential location and directional information. By considering the capabilities and limitations of these technologies, researchers and developers can create more efficient and robust control systems that can successfully operate in different environments and ensure the safety and stability of autonomous drone flight.
The use of these materials combined with state-of-the-art methods and technologies will enable the development of more robust and adaptive drone flight control systems that can function effectively in challenging environments, including communication limitations and incomplete information. Adaptive control systems are an important class of systems capable of adapting to changing environmental conditions and task requirements without the need for external intervention [Reference Javaid, Saeed, Qadir, Fahim, He, Song and Bilal23, Reference Han, Shi, Zhang, Korzhyk and Le24]. They play a key role in various fields such as industrial automation, transport, robotics, artificial intelligence and others. The classification of adaptive control systems is based on various criteria such as the ability to change parameters, structure and control strategies in response to external or internal changes (Fig. 3).
Classification of adaptive control systems. Source: compiled by the author.
Systems can be divided into static and dynamic adaptive systems. In static systems, adaptation occurs by changing the control parameters, while in dynamic systems the control structure itself is changed to adapt to changing conditions. Systems can be divided into self-adapting and self-learning systems. In self-tuning systems, control parameters are automatically adjusted based on feedback from the control object. Self-learning systems go further, they are able to change their structure and control strategies based on accumulated experience or training data. In addition, adaptive systems can be categorised according to their principle of operation. This includes feedback systems and feedforward systems. Feedback systems use information about the control error to correct actions, whereas direct-link systems operate without feedback and rely on pre-defined rules or models. The classification of adaptive control systems is an important tool for understanding and systematising different approaches to adaptation in control. It allows the selection of the most appropriate methods depending on the specific task and operating conditions.
4.0 Discussion
Adaptive algorithms for drone flight control under communication constraints and incomplete information represent a key research area in the modern aviation industry. It should be noted that drones, as autonomous systems, face various challenges in performing their tasks. One such challenge is limited communication with the base station or operator, especially in remote or poorly accessible areas. This can lead to loss of control of the drone and emergency situations. Adaptive control algorithms must be able to react to such situations and make decisions independently of external communication. Incomplete information about environmental conditions and flight parameters is another significant factor. Drones may be in various environments where access to complete and accurate information may be limited or unavailable. In such environments, adaptive algorithms must be able to adapt to the variable conditions and make decisions based on the available data, even if it is of limited reliability. Drones can be used in a variety of fields, such as surveying, agriculture, delivery, and more. Each of these fields may have unique conditions and requirements that need to be considered when developing adaptive control algorithms. One approach to addressing these challenges is to combine different methods and techniques such as prediction, decentralised algorithms, use of feedback, probabilistic methods and reinforcement learning. Their combination can provide drones with the ability to adapt to variable conditions and make informed decisions even under conditions of limited communication and incomplete information. Overall, adaptive algorithms for drone flight control under communication constraints and incomplete information are an actively developing research area that plays an important role in ensuring the reliability, safety and efficiency of autonomous flights.
According to the results of recent research by Poudel and Moh [Reference Poudel and Moh25], tasking for UAV networks is a complex process that requires a comprehensive study. It is important to define the objectives and requirements of the specific mission or task that the drones are to fulfil. This may include various aspects such as the application area (surveillance, delivery, search and rescue), time and resource constraints, and environmental features. The characteristics of the drone network, such as their number, characteristics, communication and interoperability among them, should be taken into account. Problem formulation algorithms should take these parameters into account for optimal resource allocation and coordination among the network members. It is also important to ensure efficient use of energy and flight time to maximise mission duration or to provide the required area coverage density. These findings are consistent with the theses in the previous section. Mission algorithms must be capable of adapting to changing conditions and requirements. This includes considering the dynamics of the drone environment, the ability to automatically reconfigure the network when one or more devices fail, and conflict management and problem-solving during task execution. This approach to tasking UAV networks will improve their efficiency, reliability and versatility in different usage scenarios.
Turning to the definition of Lin et al. [Reference Lin, Liu, Zhao, Wu and Wang26], an adaptive UAV deployment scheme for an emergency network is a key component of providing an effective emergency response. This scheme should be designed taking into account various factors such as type and scale of disaster, terrain topography, resource availability and communication network features. It is important to consider the possibility of loss of communication with base stations and operators during emergencies, which requires the adaptive deployment scheme to be highly autonomous and independent of centralised control. For effective operation in emergency situations, the adaptive deployment scheme should include mechanisms for dynamic route planning, as well as automatic adjustment of the deployment strategy based on incoming information on the progress of emergency operations. This will optimise the use of UAVs, ensuring that the most critical areas are covered, and emergency response times are minimised. In addition, the adaptive deployment scheme should be able to provide flexibility and scalability to effectively adapt to changing conditions and requirements during emergency operations. It should be noted that resource constraints, such as fuel and battery availability, flight range and travelling speed, should also be considered when designing an adaptive deployment scheme. Efficient utilisation of resources will extend the time in the air and increase the coverage of the accident area [Reference Umyshev, Dostiyarov, Duisenbek, Tyutebayeva, Yamanbekova, Bakhtyar and Hristov27, Reference Torepashovna, Kairbergenovna, Sergeyevich, Uyezbekovna and Kairbekovna28]. In addition, it is important to conduct regular updates and optimisations of deployment algorithms based on analysis of operational results and feedback from operators and rescue services. This will enable continuous improvement of adaptive deployment schemes and increase their effectiveness in emergency situations.
Researchers Alam et al. [Reference Alam, Arafat, Moh and Shen29] identified, that topology management in networks with multiple UAVs is a challenging task that requires the integration of interdisciplinary knowledge in information technology, unmanned aviation and network communications. A wide range of topology control algorithms, ranging from simple heuristic methods to more complex optimisation algorithms based on machine learning and artificial intelligence, are actively investigated in current research. They allow optimising the network structure, controlling data routing and resource allocation to ensure reliable and efficient communication between UAVs and base stations. An important aspect of research in this area is the adaptation of topology control algorithms to the specificities of unmanned aviation, such as dynamic changes in network composition during flight, capacity constraints and delays in data transmission, and uncertainty in the environment and flight conditions. Based on these principles, researchers aim to develop algorithms that can provide a high degree of network autonomy and robustness, as well as adaptability to changing conditions in real time. These results support the above research, as topology management in networks with multiple unmanned aircraft requires not only technical skill, but also an understanding of specific aviation processes and data transmission requirements. Research in this area seeks not only to optimise network performance under normal conditions, but also to develop adaptive strategies to respond to emergency situations such as loss of connectivity to base stations or environmental changes. The development of topology control algorithms in drone networks has a significant impact on the efficiency and security of their operation in different environments, making this an important area for further research and development.
Researchers Chen et al. [Reference Chen, Liang, Pan and Li30] have shown through his work that coordinated human-in-the-loop tracking control is an approach where the operator is included in the process of controlling the UAV system by contributing his experience and expert opinion. An improved approach to a given performance considers the importance of the cooperation between the operator and the autonomous system to achieve optimal results. This method allows the human and the autonomous system to consider the specificities of the human and the autonomous system, allowing for more effective cooperation and improved overall performance. This approach can be particularly useful in tasks that require high accuracy and responsiveness, as well as in situations where human expertise and intuition need to be taken into account for decision-making. It can be agreed with this view that the co-management of human-in-the-loop tracking with an improved approach to target performance opens new perspectives in the field of autonomous systems, providing more efficient and adaptive performance in different environments and usage scenarios.
As Zogopoulos-Papaliakos et al. [Reference Zogopoulos-Papaliakos, Karras and Kyriakopoulos31] affirm, a fault-tolerant UAV control scheme with boundary recognition represents an important aspect in flight safety and reliability. By integrating boundary recognition into the drone’s control algorithm, the ability to dynamically determine the safe flight zone and automatically correct the route in case of potentially dangerous situations is achieved. This is especially important in environments where obstacles or restrictions on the flight area, such as restricted areas or dangerous objects, may occur. This arrangement allows the drone to automatically react to changing environmental conditions and prevent accidents, making flying safer and more efficient. This is particularly relevant in the context of commercial drone applications, such as cargo delivery or infrastructure inspection, where risks need to be minimised to ensure continuity of operations and environmental protection. Analysing the results and conclusions obtained, it is clear that the integration of such a system allows the automatic adaptation of flight routes to changing environmental conditions, which significantly reduces the probability of accidents. This is important not only for commercial operations, but also for a number of other fields, including search and rescue missions, area control and monitoring of environmentally important areas. Thus, further refinement and development of fault-tolerant control schemes using flight boundary recognition systems will enhance the safety and efficiency of drone operations in various applications.
Researchers Priya and Kamlu [Reference Priya and Kamlu32] identified that a robust drone control algorithm is a key element in ensuring the safety and efficiency of drone flights. Such an algorithm must be able to provide stable and accurate control of the drone under a variety of conditions, including weather variability, obstacles and possible communication interference. Important aspects of developing a robust algorithm are its adaptability to variable conditions and its ability to automatically respond to emerging situations to minimise the risks of accidents and ensure the safety of the drone and the environment. Research into the development of robust drone control algorithms is actively pursued to improve and optimise drone performance. This research includes the development of autopiloting, navigation and trajectory control algorithms that are based on state-of-the-art machine learning and artificial intelligence techniques. As a result of successful work on such algorithms, safer and more efficient drone operations can be expected, which will facilitate their increased use in a variety of applications ranging from commercial deliveries to search and rescue missions. In addition, modern technologies such as high-fidelity sensors, machine vision systems and unmanned communication systems play a key role in developing robust drone control algorithms. The integration of these technologies enables drones to effectively interact with the environment and make informed decisions in real time based on the data received. This significantly increases the level of autonomy and adaptability of drones, making them more flexible and adaptable to different usage scenarios. Further refinement of control algorithms requires consideration of a variety of factors including safety, energy efficiency and ergonomics to ensure optimal interaction between drones and human operators or autonomous systems. The development of new approaches and techniques in this area will further advance drone technology and its successful implementation in various fields of endeavour.
5.0 Conclusions
The results of the study revealed that the development of adaptive algorithms for drone flight control under communication constraints and incomplete information is a critical task. Adaptive algorithms allow drones to function effectively based on available information and changing environmental conditions. The main results of the study emphasise the need for continued work in the development of algorithms that can adapt to variable conditions and incomplete information. This includes the use of machine learning techniques, probabilistic approaches, and data processing techniques to improve the reliability and efficiency of drone systems. In addition, the study showed that the successful application of adaptive algorithms to control drone flight under conditions of limited communication and incomplete information requires a collaborative effort from researchers, engineers and legislators. It is important to develop innovative solutions that will ensure the safety, reliability and efficiency of drone operations in different scenarios and environments.
The results of this study confirm that a generic control system capable of adapting to external disturbances can significantly improve the efficiency and reliability of autonomous systems. This is particularly important in the context of drone and other UAV applications, where various types of interference such as electromagnetic interference, weather conditions, obstacles and other factors are possible. Immunity to interference is an important aspect of ensuring reliable operation of such systems in different conditions and environments. Thus, the results of this study emphasise the importance of developing adaptive drone flight control algorithms to solve complex problems under conditions of limited communication and incomplete information. This will improve the performance of drones and expand their application domain, which ultimately contributes to the development of autonomous systems and the improvement of human life.
There are still several limitations that should be acknowledged. Additional testing would be necessary to confirm the algorithms’ performance in various scenarios and geographical settings, as the simulations that were conducted were based on certain environmental variables and geographic settings. Furthermore, the difficulties of coordinating adaptive control tactics across a fleet of drones were not thoroughly examined because the study concentrated exclusively on single-drone operations. Integration with current air traffic management systems, cybersecurity and regulatory compliance are a few more aspects that would need to be taken into account before real-world adoption. Further research should focus on optimising adaptive algorithms, considering different adaptation strategies and their impact on the performance and reliability of drone control systems in real-world applications.
