A multilayer neural dynamic controller design method of quadrotor UAV for completing time-varying tasks

A quadratic programming based neural dynamic (QPND) controller design method is proposed for time-varying trajectory tracking of unmanned aerial vehicles (UAVs). Different from the traditional controller frameworks which only use pitch and roll angles to achieve the horizontal movement of the UAV, all the three attitude angles, i.e., roll, pitch and yaw angles, are applied for horizontal moving in this paper. There are at least three great superiorities of the proposed QPND controller. Firstly, three attitude angles can be coordinated control since the quadratic programming framework incorporates three attitudes. Secondly, the new framework enables the UAV to achieve the additional performance criteria or to perform subtasks. Third, the new controller can track time-varying trajectories due to neural dynamic approach. Specifically, the design process of the controller is divided into position controller design, attitude controller design and quadratic programming formulation. Finally, simulation experiments verify the feasibility and high efficiency of the proposed controller and its superiority compared with some state-of-the-art UAV controllers.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

As a complicated multi-input, multi-output, and time-varying nonlinear system, flight control system of unmanned aerial vehicle (UAV), which determines the whole system’s performance directly, is crucial for the simulation training system design. This chapter mainly focuses on parameter identification of flight control system based on the modeling of UAVs and a specific controller design for the pendulum-like oscillation in micro aerial vehicle (MAV). A predator–prey particle swarm optimization (PSO) algorithm for identifying parameters of UAV flight control system is presented, with the aim of reducing the workload of the designers during the process of designing complicated UAV control system. Besides, a software environment for UAV controller design was developed based on the UAV model and the proposed method. Then a specific kind of controller design involving the pendulum-like oscillation in the hover and stare state for a MAV in the presence of external disturbances is discussed in detail, since the pendulum-like oscillation caused by uncertainty and external disturbances badly jeopardize the performance of hover and stare, resulting in blurred images or even MAV’s overturning. A novel type of PSO-based linear-quadratic regulator (LQR) controller for stabilizing the pendulum-like oscillation is developed, which can enhance the MAV’s performance efficiently.

An intelligent PID controller based on RBF neural network was designed for longitudinal attitude control of a small unmanned aerial vehicle (UAV) according to the feature of complexity and nonlinear; RBF neural network is used to real-time update the PID parameters on line by its learning function for achieving the longitudinal channel control of the UAV; Simulation results show that the design of the RBF neural network PID controller has better control performance and it better satisfies the requirements of the longitudinal channel flight control of small UAV.

Hybrid modeling and control issues provide a flexible powerful tool for Unmanned Aerial Vehicles (UAVs) as complex control platforms. This paper aims to briefly review recent results in this area. Firstly, existing works on hybrid modeling and control of a single UAV are reviewed. Then multiple UAVs control in a synchronously cooperative task planning, which gives much more maneuverability, has been surveyed in the hybrid framework. Formation control of UAVs is a typical cooperative strategy with considerable applications in multi-agent aerial robotics area and can leverage limited abilities of single UAVs into complicated tasks of group missions. There are a few works in this field which we briefly reviewed their results and explored the possible future directions of these research activities.

In this article simultaneous arrival of multiple unmanned aerial vehicle (UAV) at a common point was studied in a fixed network hierarchy is used. By considering the model of fixed wing UAV the decentralized based control algorithm is developed for the formation control of all UAV’s. The tree based networked geometry is developed for the formation control of multiple UAV’s with respect to the leader. Whereas, UAV’s agent or (leader / follower) are autonomously connected with each other. Formation of flight via decentralized control structure which contains two parts, one is to achieve the arrival of expected meet-point for the formation geometry and other is to maintain the synchronization of all UAV’s. Moreover, the leader is supposed and able to regulate the speed or velocity of UAV’s. Simulated results showed that arrival of multiple UAV’s at a common point for the formation is achieved successfully and the above designed control algorithm has good robustness and flexible.

This paper investigates a new scenario of spectrum sharing between unmanned\naerial vehicle (UAV) and terrestrial wireless communication, in which a\ncognitive/secondary UAV transmitter communicates with a ground secondary\nreceiver (SR), in the presence of a number of primary terrestrial communication\nlinks that operate over the same frequency band. We exploit the UAV's mobility\nin three-dimensional (3D) space to improve its cognitive communication\nperformance while controlling the co-channel interference at the primary\nreceivers (PRs), such that the received interference power at each PR is below\na prescribed threshold termed as interference temperature (IT). First, we\nconsider the quasi-stationary UAV scenario, where the UAV is placed at a static\nlocation during each communication period of interest. In this case, we jointly\noptimize the UAV's 3D placement and power control to maximize the SR's\nachievable rate, subject to the UAV's altitude and transmit power constraints,\nas well as a set of IT constraints at the PRs to protect their communications.\nNext, we consider the mobile UAV scenario, in which the UAV is dispatched to\nfly from an initial location to a final location within a given task period. We\npropose an efficient algorithm to maximize the SR's average achievable rate\nover this period by jointly optimizing the UAV's 3D trajectory and power\ncontrol, subject to the additional constraints on UAV's maximum flying speed\nand initial/final locations. Finally, numerical results are provided to\nevaluate the performance of the proposed designs for different scenarios, as\ncompared to various benchmark schemes. It is shown that in the quasi-stationary\nscenario the UAV should be placed at its minimum altitude while in the mobile\nscenario the UAV should adjust its altitude along with horizontal trajectory,\nso as to maximize the SR's achievable rate in both scenarios.\n

Instruction sender authentication of UAV (Unmanned Aerial Vehicle) control is critical for the secure operation of UAV systems. By exploring user touch action characteristics of the user interaction with the UAV control mobile terminal, this paper proposes a novel instruction sender continuous authentication framework for UAV control systems. In particular, we first obtain the touch screen data in real time from the interaction with the mobile device screen of a user when he controls the UAV. Then we create a polynomial to fit the touch trajectory on the screen of the user, and use the least square method to obtain the optimal estimate of the polynomial coefficients. Finally, we mark the grid area covered by the polynomial to extract the user’s touch screen operation action fingerprint(OAF), and employ the fuzzy extractor method to realize the identity authentication of the sender with certain error tolerance. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate.

Ensuring UAV safety: A deep learning-based fault-tolerant approach with control barrier functions

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Historically, UAVs have been employed in ways that avoid interaction with the environment at all costs. The recent trend of increasing small UAV lift capacity and the reduction of the weight of manipulator components make the realization of mobile manipulating UAVs imminent. Despite recent work, several major challenges remain to be overcome before it will be common practice to manipulate objects from UAVs. Among these challenges, the constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for reliable manipulation. Solving this challenge will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, law enforcement, and personal assistance. Toward a solution to this challenge, this thesis describes a way forward that uses the UAV as a means to crudely position a manipulator within reach of the end-effector's goal position in the world. The manipulator then performs the fine positioning of the end-effector, rejecting position perturbations caused by UAV motions. An algorithm to coordinate the redundant degrees of freedom of an aerial manipulation system is described that allows the motions of the manipulator to serve as inputs to the UAV's position controller. To demonstrate this algorithm, the manipulator's six degrees of freedom are servoed using visual sensing to drive an eye-in-hand camera to a specified pose relative to a target while treating motions of the host platform as perturbations. Simultaneously, the host platform's degrees of freedom are regulated using kinematic information from the manipulator. This ultimately drives the UAV to a position that allows the manipulator to assume a pose relative to the UAV that maximizes reachability, thus facilitating the arm's ability to compensate for undesired UAV motions. Maintaining this loose kinematic coupling between the redundant degrees of freedom of the host UAV and manipulator allows this type of controller to be applied to a wide variety of platforms, including manned aircraft, rather than a single instance of a purpose-built system. As a result of this loose coupling, careful consideration must be given to the manipulator design so that it can achieve useful poses while minimally influencing the stability of the host UAV. Accordingly, the novel application of a parallel manipulator mechanism is described.

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

Unmanned vehicles (UAVs) are technological tools whose application is carried out in the civil and military areas for surveillance, recognition and intelligence tasks. Given its versatility and features, in this paper the implementation of an autopilot is determined, after an analysis of commercial models. Said device must comply with technical requirements for the automatic flight control of a medium-sized UAV, considering the operation scenarios in the northern border of Ecuador. In addition, the parameters that intervene in the control loops and the relevant simulations are specified. The results obtained from the experimental flights of the aircraft are reflected in graphs, where the stability of the plant properly calibrated for autonomous flight is verified.

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.

Transmission lines are important part of the power system, the safety of line equipment is related to the stability of the power system. Transmission line fault inspection is a basic work to ensure the safety of line equipment. With the rapid development of unmanned aerial vehicles(UAV), the UAV equipped with detection sensors is used for line fault inspection. It is a new type of inspection, which has the advantages of higher security, greater efficiency, more freedom, and lower consumption. This paper does research on the attitude control of the inspection UAV, and designs an auto disturbance rejection control (ADRC) controller, then performs the simulation experiments to show that the ADRC controller has good control effect on the UAV flight attitude. Considering the UAV may suffer from different types of wind interference in the process of performing tasks, this paper has conducted simulation experiment during different wind interference environment, such as, gust, gradual wind, persistent wind and random wind. The ADRC controller's simulation effect is better than PID and the ADRC controller is verified of excellent wind-interference performance.