UAV Control Research Articles

Attitude control of quadrotor UAV based on fuzzy PID control under small disturbance

This paper proposes a attitude control method based on fuzzy control. Firstly, the basic model of quadrotor UAV is constructed, then the fuzzy PID controller based on fuzzy control strategy is introduced, and finally the simulation comparison experiments under PID control and fuzzy PID control are conducted. The experimental results show that the parameters of each link in the PID control method are easy to set, but the overshoot amount is large and the adjustment time is long; The simulation model of fuzzy PID control is relatively complex, and it is difficult to reach the predetermined expectation and difficult to accurately control. However, the output of overshoot, short regulation time and the waveform peak are small. In terms of anti-interference, fuzzy PID is more adaptive than PID control.

Fast finite-time backstepping controller for a quadrotor UAV under state constraints

Quadrotors have gained popularity in a wide range of applications. In this paper, a new approach for solving the tracking control problem of quadrotors with full-state constraints is presented. The proposed method involves a backstepping control scheme integrated with a fast finite-time filter. First, necessary state transformations are performed to support the design of the finite-time filter and controller. Next, the controller is formulated based on the backstepping technique. All the state constraints are taken into consideration in the controller. However, it is well-known that the backstepping control design can lead to the “explosion of complexity” when calculating time derivatives of certain nonlinear functions. Therefore, the proposed filter comes to provide a solution for estimating the time derivatives with the estimation errors converging to zero in finite time. The closed-loop system's finite-time stability is rigorously proved using the Lyapunov theory, despite the state constraints. Simulation results demonstrate the feasibility and efficacy of the proposed method.

Design of Preset Performance Reverse Step Attitude Controller for Four-rotor UAV

Aiming at the attitude control problem in flight control of four-rotor UAV, a control scheme based on the preset performance backstepping controller (ESO-NPPCBSC) for attitude Angle of four-rotor UAV with extended state observer is proposed. A preset performance function with specified time convergence is designed to constrain the transient performance and steady-state performance of the tracking error. Compared with the traditional scheme, the preset performance function can make the tracking error of the controlled system converge to the preset precision range within the specified time, and the convergence rate can be adjusted flexibly and the error conversion function can be used to transform the tracking error without constraints. The attitude Angle tracking error satisfies the preset performance condition by controlling the conversion error. The control law of attitude Angle of four-rotor UAV is designed based on reverse step method, which solves the problem of low control accuracy of four-rotor UAV under the condition of uncertain interference.

Low-level control with actuator dynamics for multirotor UAVs

PurposeA low-level controller is critical to the overall performance of multirotor unmanned aerial vehicles. The purpose of this paper is to propose a nonlinear low-level angular velocity controller for multirotor unmanned aerial vehicles in various operating conditions (e.g. different speed and different mode).Design/methodology/approachTo tackle the above challenge, the authors have designed a nonlinear low-level controller taking the actuator dynamics into account. The authors first build the actuator subsystem by combining the actuator dynamics with the angular velocity dynamics model. Then, a recursive low-level controller is developed by designing a high-gain observer to estimate unmeasurable states. Furthermore, a detailed stability analysis is given with the Lyapunov theory.FindingsSimulation tests and real-world flying experiments are provided to validate the proposed approach. In particular, we illustrate the performance of the proposed controller using violent random command test, attitude mode flight and high-speed flight of up to 18.7 m/s in real world. Compared with the classical method used in PX4 autopilot and the estimation-based incremental nonlinear dynamic inversion method, experimental results show that the proposed method can further reduce the control error.Research limitations/implicationsLow-level control of multirotor UAVs is challenging due to the complex dynamic characteristics of UAVs and the diversity of tasks. Although some progress has been made, the performance of existing methods will deteriorate as operating conditions change due to the disregard for the electromechanical characteristics of the actuator.Originality/valueTo solve the low-level angular velocity control problem in various operating conditions of multirotor UAVs, this paper proposes a nonlinear low-level angular velocity controller which takes the actuator dynamics into account.

Angular rate enhanced Overload Controller Design for Low-speed UAVs

This paper investigates the overload controller for low-speed UAV maneuvering flight. Compared with high-speed and supersonic vehicles, low-speed UAVs are more sensitive to atmospheric disturbances during maneuvering and their signals are more sensitive. This paper combines UAV overload control theory and L1 adaptive theory to propose a longitudinal attitude control method based on overload control for low-speed UAVs. The method uses overload as the outer loop and pitch angle rate as the inner loop, and provides a reliable control scheme for maneuvering low-speed UAVs. Through simulation experiments, the controller proposed in this paper is compared with the conventional classical three-loop overload controller. The results show that the controller of this paper has faster angular rate response and stronger anti-interference capability under the same overload corresponding conditions.

Target Tracking Control of UAV Through Deep Reinforcement Learning

This study presents an innovative reinforcement-learning-based control algorithm for a vertical take-off and landing (VTOL) aircraft under wind disturbances. In our approach, the tracking control problem of the VTOL aircraft is formulated as a Markov decision process, and the appropriate system state, reward function, and soft update method are presented. To improve the control accuracy under wind disturbances, three kinds of wind fields were added in the learning environment to expand the exploration space and simulate the effect of wind disturbances on the flight control. Moreover, to ensure the tracking accuracy and the practical implementation, a quantum-inspired experience replay strategy was developed based on quantum computation theory. In this strategy, the preparation operation scheme was designed to encourage the exploration and speed up the convergence. The depreciation operation method was developed to enrich the sample diversity, which increased the robustness of the controller and allowed the control strategy learned in the numerical simulations to be directly transferred into real-world environments. Numerical simulations, hardware-in-the-loop experiments, and real-world flight experiments were conducted to evaluate the performance and merits of the proposed method. The results demonstrated high accuracy and effectiveness and good robustness of the proposed control algorithm in terms of standoff target tracking control and flight stability.

Path Following for UAV using Nonlinear Model Predictive Control

For the fixed-wing UAV control system, the lateral path-following system is an important part, which ensures that the UAV flies stably in accordance with the predetermined route within a certain height. For the lateral path following of UAV based on nonlinear model predictive control, the model of the lateral kinematics of the fixed-wing UAV is established first, and then the path-following problem of UAV at a constant height is considered. The objective function with constraints based on the NMPC is established, and finally the nonlinear optimization algorithm is used to minimize the designed objective function. The optimal control quantity in the rolling interval is obtained. Matlab is used to simulate the designed controller and compare it with the commonly used L1 guidance law, and the results of the simulation demonstrate that the NMPC has superior control leverage.

Comparison of anti-interference ability between PID controller and ADRC controller in UAV operation at ocean

With the maturity of UAV technology, drones can carry different instruments in the air to help people complete their work more efficiently. However, different working environments also bring different challenges to UAV control systems. This paper mainly discusses the quadrotor UVA and compares the stability of the Proportional Integral Derivative (PID) controller and Active disturbance rejection controller (ADRC) under the disturbance of gusts at sea. The flight principle of the quadrotor and the dynamic model of the quadrotor will be discussed on this basis. Then the composition and mathematical formula of the PID and ADRC controllers are introduced and compared. In general, this paper focused on the anti-jamming ability of different controllers under the influence of gust, which shows that although the ADRC controller has a more complex system and tedious parameter adjustment process in comparison with the PID controller, it has excellent anti-gust interference ability and can better serve the offshore operation of UAV.

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

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.

Nussbaum-based finite-time containment control for multi-UAVs with input saturation and velocity constraints

This paper devotes to addressing the finite-time containment control for the multi-UAVs with input saturation and velocity constraints. A novel smooth function is presented to estimate the saturation function with adjustable estimate errors. By utilizing the mean-value theorem, the containment control for UAVs with input saturation can be turned into a variable gain control problem. To address this issue, the Nussbaum-based method is used to design the controller in this paper. Besides, based on the backstepping method, the controller can constrain the velocity states of UAVs within a desired time-varying asymmetric region by limiting the values of virtual velocity commands and the Lyapunov function. Finally, the finite-time convergence of the errors and the practicability of the control law are verified by Lyapunov stability analysis and numerical simulations.

Adaptive fractional order non-singular terminal sliding mode anti-disturbance control for advanced layout carrier-based UAV

Advanced layout carrier-based UAV has grabbed increasing attention in aerospace field for its unique characteristics such as fewer casualties, lower labor costs and more capable of carrying out various tasks in shipping environment. However, the complex body structure of advanced layout UAV can bring about insufficiency in terms of stability, making it especially susceptible to changeable working circumstances. To overcome the object complexity and external disturbances mainly consisting of air turbulence that the UAV may come across, a composite adaptive fractional-order non-singular terminal sliding mode control strategy is proposed to achieve finite-time attitude tracking control and disturbance rejection. The distinctive features of the proposed strategy are embodied in active disturbance compensation, finite-time convergence, higher control accuracy and chattering suppression. The mathematical model of flying wing UAV is established first, then a fixed-time disturbance observer applying higher-order sliding mode is designed to obtain the estimation of unknown disturbance so that the carrier-based environment factors can be considered in the later control process. Next, an adaptive fractional-order non-singular terminal sliding mode controller is developed based on fractional order calculus and adaptive double power exponential reaching law to realize finite-time tracking control of advanced layout carrier UAV with great robustness against external disturbances due to the estimation from the proposed observer, and the stability analysis of closed-loop system is implemented by utilizing Lyapunov theory. Finally, a comparison study of the proposed strategy with other sliding mode control methods is presented by numerical simulation, to illustrate its feasibility and superiority.

Quadrotor’s modeling and control system design based on PID control

The quadrotor is an unmanned aerial vehicle with a compact construction and good maneuverability. In recent years, this type of aircraft has been widely developed and has a wide range of military and civil applications. Due to the dynamic characteristics of strongly coupled, non-linear, and multi-variable quadrotor UAVs, modeling the dynamics of UAVs and designing control systems has been the focus of quadrotor research. This paper takes the quadrotor UAV as the research object. Firstly, for the basic structure of the quadrotor UAV, the aircraft-body coordinate system and the earth-surface coordinate system of the UAV flight are established, and the transformation matrix of the two coordinate systems is given. Based on a series of reasonable assumptions, the non-linear system is modeled and a simulation platform is built using Simulink. Secondly, based on the constructed non-linear dynamics equations of the system, a cascade PID-based attitude controller and position controller of the UAV are designed. Finally, the designed control system was simulated in Matlab for straight-line flight and target-position flight, and obstacles were applied to the vehicle to verify the effectiveness of the control system. The final results show that the designed control system can make the quadrotor UAV stable in vertical and horizontal motion and can successfully travel to the target position. This paper provides a control method and framework for a PID controller-based quadrotor UAV control system.

Locomotion-Based UAV Control Toward the Internet of Senses

The emergence of 5th-generation networks and the introduction of the ultra-low latency Internet, namely, tactile internet, by the International Telecommunication Union (ITU), has opened up a wide range of applications. xr, holoportation, and remote control of machines are among the ones that would revolutionize the future of factories, smart cities, and digital healthcare. vr technology provides users with highly realistic visual and auditory experiences, enabling a high sense of immersion and embodiment in virtual environments. In the real world, however, we use more senses than only vision and hearing to perceive our surroundings. Particularly, tactile sensation is the only bidirectional modality that enables us to perceive and interact with the objects and surfaces around us. This paper introduces a real-life testbed uav-based system that leverages different technologies, including vr, 360∘ video streaming over 4G/5G, and edge computing to enable 6dof view with haptic feedback for a maximized immersion. The demonstration videos of the testbed are made publicly available on the following links: https://www.youtube.com/watch?v=WTUvWkDJFIQ and https://www.youtube.com/watch?v=Bmc9ddydjYY.

Prescribed-time extended state observer and prescribed performance control of quadrotor UAVs against actuator faults

The detection and accommodation of actuator faults in control systems have received considerable attention. However, the existing fault-tolerant control techniques typically require complex and computationally intensive algorithms, and the settling time cannot be pre-assigned offline, which limits the practical applicability of these techniques. To address these problems, this paper proposes a prescribed-time fault-tolerant tracking control scheme for accommodating actuator failures in quadrotor unmanned aerial vehicles. First, a prescribed-time nominal controller is developed to precisely track the desired position in the absence of actuator faults. Subsequently, an extended state observer is developed to precisely estimate the uncertainties derived from actuator failures. Using the estimated values, a prescribed-time tracking control scheme is designed for the position subsystem, based on the prescribed performance control technique. In addition to having a simple structure, the proposed controller relaxes the limits that both the settling time and desired transient- and steady-state performances cannot be pre-assigned. Similarly, an observer-based prescribed-time tracking control scheme is established for the attitude subsystem under actuator failures. Numerical simulations are performed to validate the effectiveness and robustness of the proposed control method.

Genetic Algorithms Optimized Adaptive Wireless Network Deployment

Advancements in UAVs have enabled them to act as flying access points that can be positioned to create an interconnected wireless network in complex environments. The primary aim of such networks is to provide bandwidth coverage to users on the ground in case of an emergency or natural disaster when existing network infrastructure is unavailable. However, optimal UAV placement for creating an ad hoc wireless network is an NP-hard and challenging problem because of the UAV’s communication range, unknown users’ distribution, and differing user bandwidth requirements. Many techniques have been presented in the literature for wireless mesh network deployment, but they lack either generalizability (with different users’ distributions) or real-time adaptability as per users’ requirements. This paper addresses the UAV placement and control problem, where a set of genetic-algorithm-optimized potential fields guide UAVs for creating long-lived ad hoc wireless networks that find all users in a given area of interest (AOI) and serve their bandwidth requirements. The performance of networks deployed using the proposed algorithm was compared with the current state of the art on several experimental simulation scenarios with different levels of communication among UAVs, and the results show that, on average, the proposed algorithm outperforms the state of the art by 5.62% to 121.73%.

Finite-time prescribed performance optimal attitude control for quadrotor UAV

In this paper, the optimal attitude control problem is investigated for quadrotor UAV with time-varying control effectiveness faults based on adaptive dynamic programming. Initially, a novel nonlinear conversion function is introduced to achieve the finite-time prescribed performance on tracking errors of three attitude angles of the quadrotor UAV. Furthermore, given that the controller may experience a degree of time-varying faults due to internal component aging or external environmental influences, an improved value function with a discount factor is proposed to resist the aforementioned time-varying control effectiveness faults. Then, by integrating the concurrent learning technique into adaptive dynamic programming algorithm, a valid approximate optimal control protocol is presented. Such an algorithm not only effectively improves the utilization of historical data in the history stack but also loosens the strict persistent excitation condition into a rank condition that is easy to check online. Moreover, it can be proved that the uniformly ultimately bounded stability of the attitude system is ensured even in the presence of time-varying control effectiveness faults. Meanwhile, tracking errors of attitude angles successfully converge to pre-defined symmetric or asymmetric precision intervals with an adjustable convergence rate in a user-defined finite time. Finally, a simulation example of the quadrotor UAV attitude system is given to verify the validity of the developed control scheme.

DRL: Dynamic rebalance learning for adversarial robustness of UAV with long-tailed distribution

Adversarial robustness has attracted extensive studies in various fields by increasing the interpretability of deep learning and enhancing the understanding of neural network models. In realistic scenarios such as UAV control system, imbalanced datasets are a consensus. Therefore, how to solve the adversarial robustness on imbalanced datasets is a more and more inescapable problem in UAV control system. There have been some works on adversarial robustness on imbalanced datasets, which bring us a deeper understanding of vulnerability of deep neural networks and the generation of adversarial examples. To adjust the classification plane after training, the long-tailed robustness framework is usually designed to be multi-stage, and different classifiers are used in different stages, which can improve the robustness through multi learning. The existing methods are hardly considered to effectively handle the long-tailed robustness problem in UAV control system. To explore the intrinsic features of long-tailed robustness, we propose a one-stage robustness framework. First, we study different classifiers and propose a general cosine classifier. By changing the general cosine classifier adaptively, the model obtains a more robust classification. Then, we analyze the scalability of the focal loss and design a focal-margin loss. Finally, we design a category focus mobile learning strategy, obtained more robust features by changing the learning emphasis with this strategy. From this, we design a simple and efficient one-stage dynamic adversarial robustness method DRL under long-tailed distribution, which consists of an adaptive cosine classifier and a focal-margin loss under long-tailed mobile learning. The extended experiments demonstrate the superiority of our approach over other state-of-the-art methods, and the effectiveness of the designed module. This method can effectively solve the long-tailed robustness problem on UAV control system and other terminals.

Path tracking control of solar-powered UAV based on energy management strategy

To coordinate the conflict between the high-altitude task and long-term energy efficiency of high-altitude long endurance solar UAV, it is one of the core issues of UAV control. A strategy combining the energy management with the route tracking is presented. Firstly, based on the three-dimensional particle motion model, energy storage battery model and solar energy acquisition model for the solar UAV, according to the aerodynamic parameters of the solar UAV and the typical horizontal flight, climb, descent and other motion processes of the solar UAV, the energy management strategy of the solar UAV is designed, and the allocation mechanism and optimal flight parameters of the energy acquisition, storage and consumption of the solar UAV in different flight stages are determined. The surplus solar energy is stored by using the gravitational potential energy, and then the energy management based on height adjustment is carried out to realize the longitudinal tracking control of the solar UAV under different lighting and energy conditions. Then based on the task requirements of the lateral movement of the solar UAV, a track tracking control method based on the feedback linearization method is established by decoupling the particle dynamics equation of the solar UAV, and the track tracking control of the solar UAV in the lateral direction is realized. Finally, a simulation throughout 24 hours is implemented and illustrated the effectiveness of the energy management strategy and route tracking control law.

Distributed bearing-based formation maneuver control of fixed-wing UAVs by finite-time orientation estimation

This paper investigates the bearing-based formation maneuver control problem for a group of fixed-wing unmanned aerial vehicles. Unlike the existing works that only steer UAV swarm to achieve a fixed geometric pattern, our objective is to enable UAV swarm to maneuver as a group such that the centroid, scale and orientation of the formation can be continuously changed. The underlying graph of UAV swarm is assumed to satisfy a leader-first-follower structure, and the desired formation maneuvering parameters are only accessible to the leader. To address such issue, a cascaded control scheme is proposed. Firstly, we propose a distributed quaternion-based observer for each follower to estimate the desired orientation of the formation in finite time. Secondly, a bearing-based formation control law is developed to achieve the maneuvering formation, in which an adaptive-gain finite-time disturbance observer is synthesized to reject the deformations which vary the bearing constraints. It is demonstrated that the closed-loop formation tracking error converges to zero exponentially. Comparative simulations are preformed to demonstrate the effectiveness of the proposed method.

Robust Control of UAV with Disturbances and Uncertainty Estimation

In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order sliding mode (HOSM) estimators to estimate these perturbations that can be canceled by the control, thus improving the dynamic behavior of the controlled system. Its performance is evaluated making use of a Simcenter Amesim quadrotor based on physical models generated from experimental data in a co-simulation framework with Matlab–Simulink used to implement the designed controller with FPGA implementation. A challenging and generic maneuver with time-varying wind disturbances and uncertainty model parameters is considered.