Rapid prototyping flight test environment for autonomous unmanned aerial vehicles

Test facility is essential for most engineering research activities, from modelling and identification to verification of algorithms/methods and final demonstration. It is well known that flight tests for aerospace vehicles are expensive and quite risky. To overcome this, this paper describes a rapid prototyping platform for autonomous unmanned aerial vehicles (UAV) developed at Loughborough University, where a number of unmanned aerial and ground vehicles can perform various flight and other missions under computer control. Flexibility, maintainability and low expenses are assured by a proper choice of vehicles, sensors and system architecture. Among many other technical challenges, precision navigation of the unmanned vehicles and system integrations of commercial-off-the-shelf components from different vendors with different operational environments are discussed in detail. Matlab/Simulink based software development environment provides a seamless rapid prototyping platform from concept and theoretic developments to numerical simulation and finally flight tests. Finally, two scenarios performed by this test facility are presented to illustrate its capability

Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Autonomous ground and aerial vehicle can lead to favorable improvements in management by performing in-field tasks in a time-effective way. Greater benefits can be achieved by allowing cooperation and collaborative action among Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). A multi-phase approach is here proposed, where each unmanned vehicle involved has been conceived and will be designed to implement innovative solutions for automated navigation and infield operations within a complex irregular and unstructured scenario as vineyards in sloped terrains

Quadrotor multi-model for control purposes

In this work, a multi-model of a quadrotor is developed in order to control this system. The kinematic model of each part of the quadrotor will be derived using the Euler angles, and also the dynamics model of the quadrotor will be calculated based on the first principles of a rigid body using the Newton-Euler formulation. Furthermore, the following assumptions are used :1) The structure is completely rigid and perfectly symmetric. 2) The center of mass is in the origin of the quadrotor fixed frame. 3) The thrusts are proportional to the square of the motors rotational speed. A state-space model (kinematics and dynamics) is developed by physical laws. But, this deduced model presents several no linearities that are produced by three factors: the orientation (Pitch, Roll and Yaw), the control action and the angular velocities. To be able to control the quadrotor system in simple, linear and manageable way, it is necessary to linearize the system. Two method are possible: a classical linearization around several set-points and a multi-model linearization. In this case, a multi-model linearization is proposed due to the obtained control model will be used to compute a multi-model controller using fuzzy techniques. Fuzzy control techniques are suitable for linear parameter varying systems with no linearities, as our quadrotor.Peer ReviewedPostprint (published version

Model Predictive Control for Micro Aerial Vehicles: A Survey

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations

TRAJECTORY GENERATION BASED GUIDANCE AND CONTROL OF ROTORCRAFT UNMANNED AERIAL VEHICLES

Ph.DDOCTOR OF PHILOSOPH

A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor

In this article, a control strategy approach is proposed for a system consisting of a quadrotor transporting a double pendulum. In our case, we attempt to achieve a swing free transportation of the pendulum, while the quadrotor closely follows a specific trajectory. This dynamic system is highly nonlinear, therefore, the fulfillment of this complex task represents a demanding challenge. Moreover, achieving dampening of the double pendulum oscillations while following a precise trajectory are conflicting goals. We apply a proportional derivative (PD) and a model predictive control (MPC) controllers for this task. Transportation of a multiple pendulum with an aerial robot is a step forward in the state of art towards the study of the transportation of loads with complex dynamics. We provide the modeling of the quadrotor and the double pendulum. For MPC we define the cost function that has to be minimized to achieve optimal control. We report encouraging positive results on a simulated environmentcomparing the performance of our MPC-PD control circuit against a PD-PD configuration, achieving a three fold reduction of the double pendulum maximum swinging angle.This work has been partially supported by FEDER funds through MINECO project TIN2017-85827-P, and project KK-202000044 of the Elkartek 2020 funding program of the Basque Government. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720

Neural-Networks Control for Hover to High-Speed-Level-Flight Transition of Ducted Fan UAV With Provable Stability

In this paper, we focus on the transition control of a ducted fan vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). To achieve a steady transition from hover to high-speed flight, a neural-networks-based controller is proposed to learn the system dynamics and compensate for the tracking error between the aircraft dynamics and the desired dynamic performance. In prior, we derive the nonlinear system model of the aircraft full-envelope dynamics. Then, we propose a novel neural-networks-based control scheme and apply it on the underactuated aircraft system. Key features of the proposed controller consist of projection operator, state predictor and dynamic-formed adaptive input. It is proved and guaranteed that the tracking errors of both state predictor and neural-networks weights are upper bounded during the whole neural-networks learning procedure. The very adaptive input is formed into a dynamic structure that helps achieve a reliable fast convergence performance of the proposed controller, especially in high-frequency disturbance conditions. Consequently, the closed-loop system of the aircraft is able to track a certain trajectory with desired dynamic performance. Satisfactory results are obtained from both simulations and practical flight test in accomplishing the designed flight course