1. Introduction

The rapid advancement of automation technologies has led to a growing reliance on stabilization algorithms to ensure the functionality of intelligent devices. These algorithms are pivotal in maintaining balance in systems like UAVs and handheld devices. UAVs, for example, face challenges such as wind disturbances, making stabilization algorithms essential for accurate flight control and stability. The algorithms enable these devices to autonomously adjust their posture and mitigate external forces.

The primary objective of this research is to systematically analyze stabilization algorithms applied in various industries, particularly UAVs, and identify areas for future optimization. With the increasing demand for autonomous systems, improving the performance and precision of stabilization algorithms is key to advancing industries like robotics, aerospace, and autonomous vehicles.

2. Applications of stabilization algorithms

2.1. Unmanned Aerial Vehicles

UAVs rely on stabilization algorithms to maintain flight stability under adverse environmental conditions. Their flight control systems process real-time data from accelerometers, gyroscopes, and magnetometers to adjust motor speeds, compensating for wind gusts and payload variations. This is particularly important in applications like aerial photography and logistics, where precision and balance are crucial.

Sensor fusion techniques enhance reliability by combining data from multiple sensors to provide a comprehensive view of the UAV's surroundings. However, the complexity of real-time sensor processing can increase computational demands, especially in UAVs carrying specialized payloads.

2.2. Handheld devices

Stabilisation algorithms are a common feature of handheld devices such as gimbals and cameras, with the aim of reducing image jitter and enhancing the user experience. In these devices, stabilisation algorithms utilise data from accelerometers and gyroscopes to mitigate the effects of vibrations caused by hand movement, thereby ensuring that images remain stable and clear during use [1]. These algorithms have been widely adopted in consumer electronics, enhancing video quality by compensating for unintentional movements [2].

2.3. Smart chassis systems

Smart chassis systems, employed in autonomous vehicles and robots, depend on stabilization algorithms for navigation and obstacle avoidance. These systems use sensors such as LiDAR and infrared to maintain stability while adjusting to environmental changes. The integration of Simultaneous Localization and Mapping (SLAM) algorithms with stabilization techniques improves system adaptability.

However, a balance must be struck between sensor cost and accuracy. For instance, LiDAR provides precise mapping but is expensive, while lower-cost alternatives like infrared sensors may require more advanced algorithms to achieve similar outcomes.

3. Stabilization algorithms

Multi-sensor fusion algorithms merge data from various sensors, such as accelerometers and gyroscopes, to create control signals for real-time posture adjustment. By cross-referencing data from different sources, these algorithms reduce sensor inaccuracies, enhancing system reliability. This method is crucial in unmanned aerial vehicles (UAVs) and autonomous vehicles, where real-time adjustments to external conditions are necessary. Although relatively simple and cost-effective, adapting these algorithms to rapidly changing environments is challenging, and their real-time computational demands may limit their use in resource-constrained systems [3].

3.1. Principles

Stabilization algorithms, based on feedback control theory, monitor the system’s state and make necessary adjustments to counteract external disturbances. These algorithms use data from multiple sensors and apply data fusion techniques to estimate system states. For instance, in quadcopters, an Extended State Observer (ESO) is used to estimate and compensate for disturbances in real-time, continuously adjusting rotor speeds to maintain stability. Enhanced Active Disturbance Rejection Control (ADRC) strengthens these algorithms by improving resilience to uncertain disturbances, critical in nonlinear, underactuated systems like quadcopters [4].

3.2. Features

Key characteristics of stabilization algorithms are real-time responsiveness, high precision, and robustness. These algorithms rapidly respond to external disturbances, such as adjusting rotor speeds within milliseconds when a drone encounters strong winds, ensuring stable flight. The algorithms use high-precision sensors and technologies like Kalman filters to integrate data and minimize noise for precise control of attitude and position. Their robustness is bolstered by ADRC, allowing stable operation in complex environments [5].

3.3. Advantages

Stabilization algorithms significantly enhance system robustness by adjusting control parameters based on real-time monitoring of system states and environmental changes. Drones handle disturbances like wind and temperature fluctuations effectively. Systems using ADRC and ESO estimate disturbances in real-time and adjust accordingly, offering precise control in complex environments [6]. Stabilization algorithms dynamically adapt to environmental changes, outperforming traditional Proportional Integral Derivative (PID) controllers, especially in nonlinear systems. In situations where environmental conditions change rapidly, such as sudden wind shifts, stabilization algorithms maintain continuous correction to ensure system stability [7].

3.4. Limitations

The primary limitations of stabilization algorithms are their computational complexity, reliance on sensor accuracy, and performance in extreme environments. The algorithms often require complex sensor fusion techniques and nonlinear control theories, especially when combined with deep learning, which increases computational demands. Limited computational resources can restrict their application in embedded systems. Additionally, sensor accuracy directly impacts algorithm performance; sensor noise or delays can degrade system effectiveness. In extreme conditions like strong winds or electromagnetic interference, sensor data accuracy decreases, which may lead to algorithm failure [8].

3.5. Future optimization directions

Future improvements in stabilization algorithms should focus on optimizing sensor fusion, enhancing self-learning capabilities, and utilizing distributed and edge computing. Incorporating advanced sensor fusion techniques, such as deep learning, will improve real-time performance in complex environments. Machine learning, particularly reinforcement learning, can enable systems to autonomously learn and optimize control strategies. Distributed and edge computing will also reduce the load on central processors, improving the performance of sensor fusion algorithms in real-time applications [9].

4. Deep learning-based control

Deep learning-based control algorithms leverage neural networks to process vast amounts of data and make real-time adjustments to maintain system stability. This approach offers greater flexibility in handling unexpected disturbances, such as wind shifts or terrain changes. By learning from data, these algorithms improve performance over time, offering more effective responses than traditional methods. However, deep learning algorithms are computationally demanding, requiring high-performance hardware for real-time implementation.

4.1. Principles

In UAV technology, deep learning is primarily applied through neural networks that integrate computer vision, reinforcement learning, and adaptive control, enabling autonomous decision-making. Convolutional neural networks (CNNs) are widely used for image processing and target recognition, analyzing image data from UAV cameras to detect anomalies. For path planning and autonomous navigation, deep reinforcement learning (DRL) allows UAVs to navigate complex environments by learning and optimizing strategies. Multi-agent deep learning models also enable UAVs to collaborate in formation management, completing tasks like obstacle avoidance and path planning in real-time.

4.2. Features

Deep learning in UAVs offers high autonomy, adaptability to dynamic environments, and the ability to handle complex tasks. UAVs can autonomously learn and make decisions without human intervention, adapting to environmental changes in real-time. This is critical for tasks like target detection and path planning. Additionally, multi-task learning allows UAVs to perform several tasks simultaneously, enhancing operational efficiency in long-duration missions. However, deep learning models require large amounts of labelled data, which is often challenging to obtain, and high computational power for real-time processing.

4.3. Advantages

Deep learning greatly enhances the autonomy and task execution efficiency of UAVs. By learning from vast datasets, UAVs can make decisions autonomously, reducing reliance on human control. This is essential for time-sensitive operations, such as search-and-rescue missions, where real-time adaptability is critical. Deep learning also allows UAVs to perform multiple tasks simultaneously, increasing operational precision in complex environments like agriculture and infrastructure monitoring.

4.4. Disadvantages

Despite its advantages, deep learning in UAVs faces challenges such as the need for large, high-quality datasets, which can be difficult to acquire in certain scenarios. Moreover, deep learning models are computationally intensive, making real-time processing difficult in resource-constrained UAVs. The adaptability of these models to rapidly changing environments also requires further improvement to ensure stability in extreme conditions.

4.5. Future directions for improvement

To overcome these challenges, future research should focus on reducing model complexity through techniques like model compression and pruning, making deep learning more suitable for embedded systems. Multi-modal data fusion, combining data from various sensors, can also enhance UAVs' ability to perceive complex environments. Additionally, adaptive and online learning techniques will enable UAVs to adjust models in real-time, improving performance in dynamic conditions. Continued advancements in data augmentation and simulation training will also enhance model robustness, while ensuring the security and dependability of deep learning models in real-world applications [10].

5. Optimization strategies

5.1. Improving UAV self-stabilization algorithms

For UAVs, achieving robust self-stabilization is crucial to ensure both the safety of flight and the successful execution of missions, especially in complex or dynamic environments. Existing self-stabilization algorithms, primarily based on the PID control method, often struggle with unexpected environmental changes or external disturbances. These limitations can compromise flight stability, especially when UAVs encounter sudden shifts in wind speed, turbulence, or other unpredictable factors.

One promising solution is the integration of the Extended State Observer (ESO) into UAV control systems. An ESO can significantly enhance a UAV’s ability to reject disturbances by dynamically observing unknown disturbance factors and treating them as state variables for compensation. This method can address the shortcomings of ADRC systems, which are often ineffective in handling high-frequency or complex disturbances. The addition of ESO allows UAVs to adapt more quickly to sudden environmental shifts, particularly during adverse weather conditions like strong winds, enabling safer and more reliable operations.

The nonlinear dynamics of UAVs pose challenges for traditional controllers. Future improvements in self-stabilization algorithms should incorporate nonlinear control strategies to address this complexity. One such approach is Non-singular Terminal Sliding Mode Control (NTSMC), which uses a specially designed switching function to maintain control stability without the singularity problems that affect conventional sliding mode control techniques. NTSMC improves both responsiveness and precision, especially during intricate flight maneuvers such as rapid altitude changes, obstacle avoidance, or tight turns.

UAV self-stabilization algorithms need to adapt to uncertainties in system models or operating conditions. Adaptive control techniques enable UAVs to automatically adjust control parameters in real-time, optimizing performance across a variety of tasks and environmental conditions. For example, when a UAV carries different payloads or operates at varying speeds, the adaptive control system can automatically recalibrate to maintain stable flight. Integrating adaptive control with ADRC provides a comprehensive solution, significantly improving both the stability and accuracy of UAVs in challenging and variable environments.

5.2. Enhancing deep learning applications in UAVs

As deep learning technologies continue to evolve, their application in UAVs is expanding to include more sophisticated tasks, such as target recognition, autonomous navigation, and real-time path planning. However, despite its potential, the application of deep learning in UAVs still faces notable challenges related to the large volume of training data required, the limited computational power available in UAVs, and the ability to adapt to complex, ever-changing environments.

Deep learning models are often dependent on vast amounts of high-quality, labelled data for effective training. In many UAV application scenarios, especially those involving complex or dynamic environments, acquiring sufficient labelled data is a significant challenge. To overcome this obstacle, techniques such as data augmentation can be employed. Generative Adversarial Networks (GANs) are particularly useful for creating synthetic datasets that expand the training base, thus allowing UAVs to train even when real-world data is scarce. Additionally, UAV simulators can be utilized to create virtual flight environments, providing extensive scenarios for model training and helping to mitigate the shortage of real-world data [11].

Another major challenge is the limited computing power in UAV embedded systems, which can restrict the deployment of resource-intensive deep learning models. To address this, lightweight models need to be developed. Techniques like model compression and pruning can reduce the computational complexity of deep learning algorithms, ensuring that UAVs can efficiently execute tasks like object detection, path planning, and autonomous decision-making in real-time. For instance, lightweight versions of models such as YOLO (You Only Look Once) have proven effective in real-time object detection, and similar methodologies can be extended to other UAV tasks, including autonomous navigation.

DRL holds significant promise for enhancing UAV capabilities in path planning and decision-making. However, traditional reinforcement learning approaches often suffer from slow learning speeds and poor convergence when applied to complex, multidimensional environments. One potential solution is knowledge-driven deep reinforcement learning, which combines expert knowledge with online learning to improve learning efficiency. For instance, genetic algorithms can be used to develop initial path-planning strategies, which are then refined using deep reinforcement learning. This hybrid approach accelerates learning, enabling UAVs to make more autonomous decisions in complex tasks, such as navigating urban environments or avoiding dynamically moving obstacles [12].

5.3. Future directions for combining self-stabilization algorithms and deep learning

Looking ahead, the combination of UAV self-stabilization algorithms and deep learning technologies will play a critical role in advancing UAV capabilities. As UAVs are required to operate in increasingly complex environments, integrating self-stabilization with deep learning will enhance both stability and intelligent decision-making.

Deep learning can improve the adaptive capabilities of self-stabilization algorithms by analyzing environmental data in real-time and adjusting control parameters dynamically. For example, when a UAV encounters high wind speeds, a deep learning model could optimize the stabilization control strategy based on sensor feedback, ensuring that the UAV remains stable despite the disturbance. Implementing deep learning-based adaptive stabilization algorithms will significantly improve UAV resilience and flight stability in challenging conditions [13].

In multi-UAV formation flight, deep learning combined with self-stabilization algorithms can enhance cooperative control. By analyzing relative positions within the formation and interpreting environmental data in real-time, UAVs can intelligently adjust their flight attitudes and paths to maintain optimal formation and avoid collisions. This capability will be especially valuable in applications such as military missions, logistics coordination, and rescue operations, where efficiency and precision across multiple UAVs are critical.

Future developments should focus on integrating real-time data feedback and online learning mechanisms, allowing UAVs to continuously learn and adjust during flight. For example, deep learning models could adapt UAV flight strategies based on real-time sensor data, enabling the UAV to respond more effectively to sudden changes in the environment, such as obstacles or weather shifts. Incorporating online learning will enhance UAV autonomy, enabling them to operate more effectively in dynamic and complex scenarios like disaster relief, unmanned deliveries, or long-duration surveillance missions [14].

6. Conclusion

The advancements in UAVs rely heavily on the development and refinement of stabilization algorithms and deep learning technologies. Stabilization algorithms, particularly those utilizing multi-sensor fusion, play a crucial role in maintaining UAVs' flight stability amidst external disturbances, such as wind gusts or uneven payload distribution. By integrating data from accelerometers, gyroscopes, and other sensors, these algorithms ensure UAVs can perform complex maneuvers while maintaining balance and precision, which is critical for applications like aerial photography and logistics.

However, the real-time processing of multi-sensor data can impose significant computational demands, especially for UAVs operating in resource-constrained environments or carrying specialized payloads. While ESO and ADRC offer significant improvements in handling external disturbances, nonlinear control strategies and adaptive algorithms are needed to address the dynamic and uncertain nature of UAV flight.

Deep learning further enhances UAV capabilities by enabling real-time decision-making, path planning, and target recognition. The integration of CNNs and DRL allows UAVs to navigate complex environments autonomously, making them more adaptable and efficient. Despite these advancements, deep learning faces challenges, such as the high demand for labelled training data and the computational limitations of embedded systems. Techniques like model compression, data augmentation, and multi-modal data fusion are essential for making deep learning models more practical for UAVs.

The impact of these technologies extends beyond UAVs to other domains, such as handheld devices and smart chassis systems, where stabilization algorithms improve performance and user experience. In UAVs, the future lies in combining self-stabilization with deep learning, offering more intelligent and adaptive control mechanisms. UAVs will be able to operate in increasingly complex environments, utilizing real-time sensor data and online learning mechanisms to make autonomous decisions, enhancing their capabilities in disaster relief, military missions, logistics, and beyond.

In summary, the continued optimization of stabilization algorithms and deep learning technologies will significantly elevate UAV performance. Future research should focus on overcoming computational constraints, improving sensor fusion, and integrating adaptive learning mechanisms. This will enable UAVs to maintain stability and perform complex tasks autonomously, even in the most challenging environments.