Study for the modelling and control of a coaxial helicòpter Unmaned Aerial Vehicle (UAV)

El present document es tracta de la memòria del Treball Final de Màster dels estudis de Màster Universitari en Enginyeria de Sistemes Automàtics i Electrònica Industrial (MUESAEI), a l'Escolta Tècnica Superior d'Enginyeries Industrial i Aeronàutica de Terrassa (ETSEIAT) de la Universitat Politècnica de Catalunya (UPC). El treball consisteix en l'estudi del modelat i el control d'un helicòpter coaxial de radio control per tal de convertir-lo en un Vehicle Aeri No Tripulat (Unmaned Aerial Vehicle - UAV) capaç d'estabilitzar-se en un punt de l'espai i mantenir la seva posició davant petites pertorbacions. En aquest document en primer lloc hi trobem una introducció al helicòpter coaxial emprat, així com al laboratori de control, i el conjunt de tasques de condicionament que s’han realitzat per tal de poder treballar adequadament amb el sistema. Posteriorment, es mostra el procés per obtenir un model dinàmic no lineal de l’helicòpter coaxial del treball, així com un model linealitzat sobre el punt d’equilibri, i les simulacions d’aquests dos models. A continuació, s’ha dissenyat una estructura de control en cascada basada en un control d’actitud, un control de velocitat i un control de posició, i s’han obtingut els controladors que conformen l’estructura a partir de tècniques de control lineal basades principalment en els clàssics controladors PID. Per acabar, s’ha discretitzat i programat l’algoritme de control en MATLAB, i s’ha realitzat un conjunt d’experiments amb el sistema del laboratori, entre el quals se’n destaquen experiments en el punt d’equilibri davant pertorbacions, i experiments de seguiment de trajectòri

Implementación de un control fuzzy en el lazo de control de velocidad de un helicóptero coaxial no tripulado

[Resumen] El objetivo de este trabajo es el de estudiar el comportamiento de un controlador Fuzzy en el control de las velocidades lineales de un vehículo aéreo no tripulado, en el caso concreto de aplicación en un helicóptero coaxial en miniatura. Para ello, se parte de un sistema de control en cascada compuesto por un control de trayectoria, un control de velocidad y un control de actitud, y del cual ya se ha validado su funcionamiento de forma experimental. Este control, resultado de otro proyecto, muestra un correcto comportamiento respecto a sus especificaciones, pero demuestra una respuesta mejorable en el control de las velocidades. Por ello, en este artículo se muestran los resultados obtenidos al substituir este controlador, el cual anteriormente estaba implementado con un conjunto de controladores de tipo Proporcional- Derivativo, por un controlador inteligente de tipo Fuzzy. Los resultados muestran un comportamiento algo más rápido y más suave, por lo tanto se consigue mejorar el controlador de velocidad anterior.Este trabajo ha sido subvencionado por el Gobierno Español (MINISTERIO DE ECONOMIA Y COMPETITITVIDAD) y FEDER bajo el proyecto DPI2014- 58104-R (HARCRICS)https://doi.org/10.17979/spudc.978849749808

A deep reinforcement learning approach for path following on a quadrotor

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes the Deep Deterministic Policy Grandient (DDPG) reinforcement learning algorithm to solve the path following problem in a quadrotor vehicle. This agent is implemented using a separated control and guidance structure with an autopilot tracking the attitude and velocity commands. The DDPG agent is implemented in python and it is trained and tested in the RotorS-Gazebo environment, a realistic multirotor simulator integrated in ROS. Performance is compared with Adaptive NLGL, a geometric algorithm that implements an equivalent control structure. Results show how the DDPG agent is able to outperform the Adaptive NLGL approach while reducing its complexity.This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF) through the SCAV project (ref. MINECO DPI2017-88403-R), and by SMART project (ref. EFA 153/16 Interreg Cooperation Program POCTEFA 2014- 2020). Bartomeu Rubí is also supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, the European Social Fund (ESF) and AGAUR under a FI grant (ref. 2017FI B 00212).Peer ReviewedPostprint (author's final draft

A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer ReviewedPostprint (author's final draft

Quadrotor path following and reactive obstacle avoidance with deep reinforcement learning

A deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidance problem is proposed in this paper. The problem is solved with two agents: one for the path following task and another one for the obstacle avoidance task. A novel structure is proposed, where the action computed by the obstacle avoidance agent becomes the state of the path following agent. Compared to traditional deep reinforcement learning approaches, the proposed method allows to interpret the training process outcomes, is faster and can be safely trained on the real quadrotor. Both agents implement the Deep Deterministic Policy Gradient algorithm. The path following agent was developed in a previous work. The obstacle avoidance agent uses the information provided by a low-cost LIDAR to detect obstacles around the vehicle. Since LIDAR has a narrow field-of-view, an approach for providing the agent with a memory of the previously seen obstacles is developed. A detailed description of the process of defining the state vector, the reward function and the action of this agent is given. The agents are programmed in python/tensorflow and are trained and tested in the RotorS/gazebo platform. Simulations results prove the validity of the proposed approach.This work has been partially funded by the Spanish Government (MINECO) through the project CICYT (ref. DPI2017-88403-R).Peer ReviewedPostprint (published version

Adaptive nonlinear guidance law using neural networks applied to a quadrotor

© 2019IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.Peer ReviewedPostprint (author's final draft

Implementación de un control Fuzzy en el lazo de control de velocidad de un helicóptero coaxial no tripulado

El objetivo de este trabajo es el de estudiar el comportamiento de un controlador Fuzzy en el control de las velocidades lineales de un vehículo aéreo no tripulado, en el caso concreto de aplicación en un helicóptero coaxial en miniatura. Para ello, se parte de un sistema de control en cascada compuesto por un control de trayectoria, un control de velocidad y un control de actitud, y del cual ya se validado su funcionamiento de forma experimental. Este control, resultado de otro proyecto, muestra un correcto comportamiento respecto a sus especificaciones, pero demuestra una respuesta mejorable en el control de las velocidades. Por ello, en este artículo se muestran los resultados obtenidos al substituir este controlador, el cual anteriormente estaba implementado con un conjunto de controladores de tipo Proporcional-Derivativo, por un controlador inteligente de tipo Fuzzy. Los resultados muestran un comportamiento algo más rápido y más suave, por lo tanto se consigue mejorar el controlador de velocidad anterior.Peer Reviewe

Study for the modelling and control of a coaxial helicòpter Unmaned Aerial Vehicle (UAV)

El present document es tracta de la memòria del Treball Final de Màster dels estudis de Màster Universitari en Enginyeria de Sistemes Automàtics i Electrònica Industrial (MUESAEI), a l'Escolta Tècnica Superior d'Enginyeries Industrial i Aeronàutica de Terrassa (ETSEIAT) de la Universitat Politècnica de Catalunya (UPC). El treball consisteix en l'estudi del modelat i el control d'un helicòpter coaxial de radio control per tal de convertir-lo en un Vehicle Aeri No Tripulat (Unmaned Aerial Vehicle - UAV) capaç d'estabilitzar-se en un punt de l'espai i mantenir la seva posició davant petites pertorbacions. En aquest document en primer lloc hi trobem una introducció al helicòpter coaxial emprat, així com al laboratori de control, i el conjunt de tasques de condicionament que s’han realitzat per tal de poder treballar adequadament amb el sistema. Posteriorment, es mostra el procés per obtenir un model dinàmic no lineal de l’helicòpter coaxial del treball, així com un model linealitzat sobre el punt d’equilibri, i les simulacions d’aquests dos models. A continuació, s’ha dissenyat una estructura de control en cascada basada en un control d’actitud, un control de velocitat i un control de posició, i s’han obtingut els controladors que conformen l’estructura a partir de tècniques de control lineal basades principalment en els clàssics controladors PID. Per acabar, s’ha discretitzat i programat l’algoritme de control en MATLAB, i s’ha realitzat un conjunt d’experiments amb el sistema del laboratori, entre el quals se’n destaquen experiments en el punt d’equilibri davant pertorbacions, i experiments de seguiment de trajectòri

Deep reinforcement learning for quadrotor path following with adaptive velocity

This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep reinforcement learning theory. Three different approaches implementing the Deep Deterministic Policy Gradient algorithm are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that allows the agent to anticipate to the curves. The third agent is capable to compute the optimal velocity according to the path’s shape. A training framework that combines the tensorflow-python environment with Gazebo-ROS using the RotorS simulator is built. The three agents are tested in RotorS and experimentally with the Asctec Hummingbird quadrotor. Experimental results prove the validity of the agents, which are able to achieve a generalized solution for the path following problem.This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF) through the SCAV Project (Ref. MINECO DPI2017-88403-R), and by SMART Project (Ref. EFA 153/16 Interreg Cooperation Program POCTEFA 2014-2020). Bartomeu Rubí is also supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, the European Social Fund (ESF) and AGAUR under a FI Grant (Ref. 2017FI_B_00212).Peer ReviewedPreprin

A Survey of path following control strategies for UAVs focused on quadrotors

The trajectory control problem, defined as making a vehicle follow a pre-established path in space, can be solved by means of trajectory tracking or path following. In the trajectory tracking problem a timed reference position is tracked. The path following approach removes any time dependence of the problem, resulting in many advantages on the control performance and design. An exhaustive review of path following algorithms applied to quadrotor vehicles has been carried out, the most relevant are studied in this paper. Then, four of these algorithms have been implemented and compared in a quadrotor simulation platform: Backstepping and Feedback Linearisation control-oriented algorithms and NLGL and Carrot-Chasing geometric algorithms.Peer Reviewe