Distributed Robust Formation Tracking Control for Quadrotor UAVs with Unknown Parameters and Uncertain Disturbances

Quadrotor unmanned aerial vehicles (UAVs) have been actively used in various fields. However, only the altitude and the attitude in three degrees of freedom can be independently controlled since quadrotor UAVs are underactuated systems. A quad tilt rotor UAV solves the problem of an underactuated system in a general quadrotor UAV. The quad tilt rotor UAV can control both position and attitude independently by tilting the directions of the propellers. However, the flight control system in a wide range of attitudes has not been discussed yet, for example a UAV can fly and hover with a 90 pitch angle, and it can even flip over when the thrust direction is tilted in a wide enough range. In this paper, we present the attitude transition flight control system for pitch angles ranging from 0 to 90 since flight conditions with a 90 pitch angle significantly differs from that in a conventional quadrotor UAV flight. We construct an adequate control system for a flight with a wide range of attitude conditions.

Formation-containment control problems for unmanned aerial vehicle (UAV) swarm systems with directed interaction topologies are addressed. UAVs in the swarm system are classified into leaders and followers, where leaders and followers are designed to achieve time-varying formation and containment respectively. Based on state transformation and state space decomposition, formation-containment problems of UAV swarm systems are converted into the asymptotic stability problems. Sufficient conditions for UAV swarm systems to achieve formation-containment are proposed, where the formation for leaders can be time-varying. Theoretical results are demonstrated by outdoor formation-containment experiments with five quadrotors.

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

This paper presents the control of an indoor unmanned aerial vehicle (UAV) using multi-camera visual feedback. For the autonomous flight of the indoor UAV, instead of using onboard sensor information, visual feedback concept is employed by the development of an indoor flight test-bed. The indoor test-bed consists of four major components: the multi-camera system, ground computer, onboard color marker set, and quad-rotor UAV. Since the onboard markers are attached to the pre-defined location, position and attitude of the UAV can be estimated by marker detection algorithm and triangulation method. Additionally, this study introduces a filter algorithm to obtain the full 6-degree of freedom (DOF) pose estimation including velocities and angular rates. The filter algorithm also enhances the performance of the vision system by making up for the weakness of low cost cameras such as poor resolution and large noise. Moreover, for the pose estimation of multiple vehicles, data association algorithm using the geometric relation between cameras is proposed in this paper. The control system is designed based on the classical proportional-integral-derivative (PID) control, which uses the position, velocity and attitude from the vision system and the angular rate from the rate gyro sensor. This paper concludes with both ground and flight test results illustrating the performance and properties of the proposed indoor flight test-bed and the control system using the multi-camera visual feedback.

Unmanned Aerial Vehicles (UAV) have become more popular for usage due to the low cost of deployment and maintenance. Single UAV employment allows remote area monitoring and transferring different payloads to inaccessible or dangerous zones for human. In order to deal with flight tasks that are more complex, UAV swarms are applied. The main challenge of UAV swarm formation and flight control is to avoid vehicle collisions. In this case, artificial intelligence is responsible for flight performance in the airspace in such way that collision is avoided. The main requirements to the method, which will provide conflict-free maneuvers, are safety (collision avoidance), liveness (decentralized control, destination area reachability) and flyability (UAV flight performance constraints are satisfied). Artificial force field method fulfills all of these demands. It allows to detect a potential conflict between multiple UAVs in a swarm and other static or moving obstacles found in airspace, to provide collision resolution by changing UAVs flight parameters through maintaining minimum separation distance, including cases when manned vehicles are found in the same airspace. There can be distinguished by a wide range of obstacles: static (buildings, restricted areas and bed weather conditions) and dynamic ones (other UAVs, manned aircraft). Method allows keeping UAV swarm shape on the flight path, taking into account ground speed and turn bank angle values restrictions according to UAV’s flight performance characteristics.

Fault diagnosis is critical for safe and reliable operations of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce negative impacts of faults. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying model is established to describe quadrotor UAV dynamics, which considers measurement noises and external disturbances. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and H − / L ∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L ∞ performance condition, and actuator faults are detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residual vectors and defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Over the past decade the use of Unmanned Aerial Vehicles (UAVs) for the inspection of turbine blades has been registering steady progress and is fast becoming a well-established inspection methodology especially at offshore wind farms. A UAV operating in the open field is subject to varying ambient conditions which have an effect on the power required to maintain stable flight. This may have an impact on the flight endurance of the UAV, especially when operating in windy conditions. Simulations are a very useful tool for estimating the impact of such ambient conditions on the performance and flight endurance of a UAV. However, it is extremely difficult to accurately model all the dynamics at play in the open field where flow conditions are highly stochastic. Few open field studies necessary to validate such simulation models have been carried out to date in this regard. In this study, the impact of open field wind conditions on the flight endurance of a hovering UAV is investigated. The test vehicle used in this study is a quadrotor UAV, which was fitted with an array of sensors to monitor power consumption parameters of the propulsion motors whilst the vehicle is hovering at a fixed altitude above the ground. The quadrotor was also fitted with an ultrasonic wind sensor in order to measure the relevant wind parameters that the quadrotor was being subjected to during the hovering study. The test UAV was flown in different ambient conditions to establish the impact on the UAV flight endurance when subjected to different wind speeds. Results from a series of UAV test flights in the open field indicated that the power required by the UAV to maintain hovering flight decreases as the wind speed increases.

The tilt tri-rotor unmanned aerial vehicle (UAV) has three flight modes: the hover mode, the transition mode, and the fixed-wing mode. Controller design in the hover mode is the premise of realizing stable flight of this kind of UAV. Due to the particular structure with odd rotors and strong nonlinearity, the modeling and control of the tilt tri-rotor UAV remain an active and ongoing research topic. To overcome these problems and achieve stable flight control, this paper proposes a sliding mode-based nonlinear control scheme for the hovering flight of a tilt tri-rotor UAV, consisting of position control, attitude control, and control allocation. First, the mathematical model of the UAV is given by using the Newton–Euler formulation. Second, a cascade flight controller consisting of the position controller and the attitude controller is developed based on sliding mode control (SMC). For the position controller, an auxiliary dynamic system composed of the hyperbolic tangent functions is introduced to the SMC approach for constraining the output magnitude of the thrust and the reference attitudes. Besides, a disturbance observer is applied to the attitude controller to alleviate the chattering and improve robustness. Furthermore, according to the structural characteristics of the tilt tri-rotor UAV, a control allocation algorithm is developed to map the virtual control quantities calculated by the cascade flight controller to the actual actuators. Simulations are conducted to verify the robustness against the external disturbances and parameter variations, and the performance comparisons with two other control schemes are also given. Finally, the experiment is also carried out to validate the performance of the proposed control scheme.

<div>In this dissertation, methods for real-time trajectory generation and autonomous obstacle avoidance for fixed-wing and quad-rotor unmanned aerial vehicles (UAV) are studied. A key challenge for such trajectory generation is the high computation time required to plan a new path to safely maneuver around obstacles instantaneously. Therefore, methods for rapid generation of obstacle avoidance trajectory are explored. The high computation time is a result of the computationally intensive algorithms used to generate trajectories for real-time object avoidance. Recent studies have shown that custom solvers have been developed that are able to solve the problem with a lower computation time however these designs are limited to small sized problems or are proprietary. Additionally, for a swarm problem, which is an area of high interest, as the number of agents increases the problem size increases and in turn creates further computational challenges. A solution to these challenges will allow for UAVs to be used in autonomous missions robust to environmental uncertainties.</div><div><br></div><div>In this study, a trajectory generation problem posed as an optimal control problem is solved using a sequential convex programming approach; a nonlinear programming algorithm, for which custom solver is used. First, a method for feasible trajectory generation for fast-paced obstacle-rich environments is presented for the case of fixed-wing UAVs. Next, a problem of trajectory generation for fixed-wing and quad-rotor UAVs is defined such that starting from an initial state a UAV moves forward along the direction of flight while avoiding obstacles and remaining close to a reference path. The problem is solved within the framework of finite-horizon model predictive control. Finally, the problem of trajectory generation is extended to a swarm of quad-rotors where each UAV in a swarm has a reference path to fly along. Utilizing a centralized approach, a swarm scenario with moving targets is studied in two different cases in an attempt to lower the solution time; the first, solve the entire swarm problem at once, and the second, solve iteratively for a UAV in the swarm while considering trajectories of other UAVs as fixed.</div><div><br></div><div>Results show that a feasible trajectory for a fixed-wing UAV can be obtained within tens of milliseconds. Moreover, the obtained feasible trajectories can be used as initial guesses to the optimal solvers to speed up the solution of optimal trajectories. The methods explored demonstrated the ability for rapid feasible trajectory generation allowing for safe obstacle avoidance, which may be used in the case an optimal trajectory solution is not available. A comparative study between a dynamic and a kinematic model shows that the dynamic model provides better trajectories including aggressive trajectories around obstacles compared to the kinematic counterpart for fixed-wing UAVs, despite having approximately the same computational demands. Whereas, for the case of quad-rotor UAVs, the kinematic model takes almost half the solution time than with a reduced dynamic model, despite having approximately the similar range of values for the cost function. When extended to a swarm, solving the problem for each UAV is four to seven times computationally cheaper than solving the swarm as a whole. With the improved computation time for trajectory generation for a swarm of quad-rotors using centralized approach, the problem is now reasonably scalable, which opens up the possibility to increase the number of agents in a swarm using high-end computing machines for real-time applications. Overall, a custom solver jointly with a sequential convex programming approach solves an optimization problem in a low computation time.</div>

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Quadrotor unmanned aerial vehicles (UAVs) operating in agricultural fields for aerial photography are susceptible to external disturbances. The disturbances result in trajectory deviation and irregular image overlapping that considerably degrade image quality. Disturbance observers (DOs) are commonly researched for counteracting these effects but may have delays and limitations in handling diverse and high-frequency disturbances. To this end, this work proposes a continuous high-order sliding mode-assisted DO (HSMDO) with limited time convergence characteristics for the estimation of disturbances in systems. The observer consists of a classical nonlinear DO (NDO) and a sliding mode-assisted system (SMAS). The NDO is used to estimate disturbances preliminarily. The SMAS is utilised to assist the NDO in estimating the high-frequency component of disturbances and ensure that the entire DO is finite-time convergent. Finally, the tracking controller is designed on the basis of the HSMDO, which enables UAVs to track the prescribed trajectories under disturbances stably. Simulation results show that the proposed HSMDO can accurately estimate various types of disturbances. Moreover, the tracking controller based on the HSMDO can improve the antidisturbance performance of systems and ensure the trajectory tracking accuracy of UAVs.

This paper deals with the tracking control problem of quadrotor unmanned aerial vehicles (QUAVs) with external disturbances. First, because the QUAV model contains two non-integrity constraints, the dynamic model of the QUAV is decomposed into two subsystems which are independently controlled, so as to reduce controller design complexity. Secondly, the nonlinear disturbance observer (DOB) technique is integrated into a backstepping control method to design the controller for the first subsystem, in which a DOB is applied to estimate the lumped uncertainty. Based on the double power reaching law and the DOB, a multivariable sliding mode control (MSMC) scheme is developed for the second subsystem. Thirdly, based on Lyapunov theory, the closed-loop system is proved to be asymptotically stable. Finally, our comparative simulation results demonstrate that the presented control scheme behaves better in terms of tracking performance than the adaptive backstepping control (ABC) approach.

The unmanned aerial vehicle (UAV) is one of the mobile robots which has grown rapidly in popularity in recent years. UAVs have been used in various activities to convey important information on the aerial transportation system. However, an application to get real-time information from UAVs remains a challenging task. Therefore, this paper presents an integration system to optimise the use of UAVs for aerial surveillance. The proposed system explains the design and implementation a UAV system which consists of on-board and ground stations. The on-board system has the function to send UAV flight data, send aerial images and carry items using a gripper which equipped with sensors to detect obstacles. The ground station has the function to control UAV flights, receive and display UAV flight data and aerial images, display the attitude of the UAV and send commands to move the camera and gripper. Flight controllers in this system use non-linear complementary filter on SO(3) to estimate the attitude of the UAV and proportional-integral (PI) controller to stabilise the UAV flight. By providing the real-time acquisition data of the UAV, we believe this system can be a useful tool for many implementations using UAV.

Unmanned Aerial Vehicle Technology Making them fly was the easy part. Making them useful was the next challenge. And now unmanned aerial vehicles (UAVs) are being put to the test to determine where they are the most applicable to the oil and gas industry. Companies and university researchers developing these “flying computers” believe that their sector is ready to rapidly expand, because of recent technological advancements and legislation that will open up the unmanned skies in the industry’s largest potential market by 2015: the United States. UAVs are already being used by some oil and gas companies to inspect flare stacks and track migrating wildlife and ice floes in the Arctic. In the near future, UAVs will be used as important tools to respond to oil spills and pipeline monitoring, and in offshore installation and decommissioning operations. A few years ago, the militaries of the world held a virtual monopoly on the application of UAV technology, and UAVs available to the private sector were little more than eyes in the sky with limited functionality. Today, commercial UAVs are the benefactors of miniaturized electronics, partly thanks to the smartphone industry and advanced software programs specifically designed to make sense of the different types of data that can be gathered while flying. Equipped with lasers, high-definition cameras, thermal imaging systems that can “see” at night, and an array of other sensors, advocates of UAVs claim they are not simply cheaper alternatives to fixed-wing aircraft and helicopters, but in many ways are more capable and, without question, safer to operate. Last fall, BP completed an experimental flight in Prudhoe Bay, Alaska, using a quadrotor UAV developed by Canadian-based Aeryon Labs. Quadrotor UAVs, also called quadcopters, use four rotors to lift and move the aircraft and are known for their maneuverability. At the time, US authorities were only issuing flight certificates to determine the airworthiness of UAVs without allowing commercial operations. BP completed the test run to determine how Aeryon’s UAV could inspect oilfield equipment and pipelines to assess maintenance needs.

Thanks to their hovering and vertical take-off and landing abilities, quadrotor unmanned aerial vehicles (UAVs) are receiving a great deal of attention. With the diversified development of the functions of UAVs, the requirements for flight performance with higher stability and maneuverability are increasing. Aiming at parameter uncertainty and external disturbance, a deep deterministic policy gradient-based active disturbance rejection controller (DDPG-ADRC) is proposed. The total disturbances can be compensated dynamically by adjusting the controller bandwidth and the estimation of system parameters online. The tradeoff between anti-interference and rapidity can be better realized in this way compared with the traditional ADRC. The process of parameter tuning is demonstrated through the simulation results of tracking step instruction and sine sweep under ideal and disturbance conditions. Further analysis shows the proposed DDPG-ADRC has better performance.