Diseño de un controlador LQG para un helicóptero de tres grados de libertad

This paper presents the design of a linear quadratic gaussian (LQG) controller to regulate the pitch, elevation and travel angles of a helicopter with three degrees of freedom (3 DOF). The LQG controller was designed on the basis of a linear model and the regulator parameters were adjusted based on the simulations performed with the non-linear model, evaluating the transient response of the system in the closed loop for different values of the reference signal, guaranteeing obtain the greatest variation of the angles around the equilibrium point. The experimental validation was done using a prototype built by the authors; the transient response of the simulated data was compared against the experimental data, for the three degrees of freedom of the helicopter, observing that the mathematical model adjusts to the dynamic of the prototype and the conditions of design were fulfilled.El artículo presenta el diseño de un controlador lineal cuadrático gaussiano (LQG) para regular los ángulos de cabeceo, elevación y viaje de un prototipo de un helicóptero de tres grados de libertad (3 DOF). El controlador LQG se diseña a partir de un modelo lineal y se ajustan los parámetros del regulador en base a simulaciones realizadas con el modelo no lineal, evaluando la respuesta transitoria del sistema en lazo cerrado para diferentes valores de la señal de referencia, garantizando obtener la mayor variación de los ángulos alrededor del punto de equilibrio. La validación experimental de la estrategia de control se realiza sobre un prototipo construido por los autores, la respuesta transitoria de los datos simulados se compara con los datos experimentales, para los tres grados de libertad del helicóptero, observando que el modelo matemático se ajusta a la dinámica del prototipo y se cumplen las condiciones de diseño.El artículo presenta el diseño de un controlador lineal cuadrático gaussiano (LQG) para regular los ángulos de elevación, cabeceo y viaje de un prototipo de un helicóptero de tres grados de libertad (3 DOF). El controlador LQG se ajusta en base del modelo matemático no lineal y se evalúa la respuesta transitoria del sistema en lazo cerrado para diferentes valores de la señal de referencia, ajustando los parámetros del controlador con el objetivo de estabilizar el sistema, disminuir el sobrepaso y el tiempo de establecimiento. La validación experimental de la estrategia de control se realiza sobre un prototipo construido por los autores, la respuesta transitoria de los datos simulados se compara con los datos experimentales, para los tres grados de libertad del helicóptero, observando que el modelo matemático se ajusta a la dinámica del prototipo y se cumplen las condiciones de diseño.This paper presents the design of a linear quadratic gaussian (LQG) controller to regulate the pitch, elevation and travel angles of a helicopter with three degrees of freedom (3 DOF). The LQG controller was designed on the basis of a linear model and the regulator parameters were adjusted based on the simulations performed with the non-linear model, evaluating the transient response of the system in the closed loop for different values of the reference signal, guaranteeing obtain the greatest variation of the angles around the equilibrium point. The experimental validation was done using a prototype built by the authors; the transient response of the simulated data was compared against the experimental data, for the three degrees of freedom of the helicopter, observing that the mathematical model adjusts to the dynamic of the prototype and the conditions of design were fulfilled

Development of Fault Diagnosis and Fault Tolerant Control Algorithms with Application to Unmanned Systems

Unmanned vehicles have been increasingly employed in real life. They include unmanned air vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned spacecrafts, and unmanned underwater vehicles (UUVs). Unmanned vehicles like any other autonomous systems need controllers to stabilize and control them. On the other hand unmanned systems might subject to different faults. Detecting a fault, finding the location and severity of it, are crucial for unmanned vehicles. Having enough information about a fault, it is needed to redesign controller based on post fault characteristics of the system. The obtained controlled system in this case can tolerate the fault and may have a better performance. The main focus of this thesis is to develop Fault Detection and Diagnosis (FDD) algorithms, and Fault Tolerant Controllers (FTC) to increase performance, safety and reliability of various missions using unmanned systems. In the field of unmanned ground vehicles, a new kinematical control method has been proposed for the trajectory tracking of nonholonomic Wheeled Mobile Robots (MWRs). It has been experimentally tested on an UGV, called Qbot. A stable leader-follower formation controller for time-varying formation configuration of multiple nonholonomic wheeled mobile robots has also been presented and is examined through computer simulation. In the field of unmanned aerial vehicles, Two-Stage Kalman Filter (TSKF), Adaptive Two-Stage Kalman Filter (ATSKF), and Interacting Multiple Model (IMM) filter were proposed for FDD of the quadrotor helicopter testbed in the presence of actuator faults. As for space missions, an FDD algorithm for the attitude control system of the Japan Canada Joint Collaboration Satellite - Formation Flying (JC2Sat-FF) mission has been developed. The FDD scheme was achieved using an IMM-based FDD algorithm. The efficiency of the FDD algorithm has been shown through simulation results in a nonlinear simulator of the JC2Sat-FF. A fault tolerant fuzzy gain-scheduled PID controller has also been designed for a quadrotor unmanned helicopter in the presence of actuator faults. The developed FDD algorithms and fuzzy controller were evaluated through experimental application to a quadrotor helicopter testbed called Qball-X4

Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions

This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented

Design and construction of a portable force-reflecting manual controller for teleoperation systems

A man-machine system called teleoperator system has been developed to work in hazardous environments such as nuclear reactor plants. Force reflection is a type of force feedback in which forces experienced by the remote manipulator are fed back to the manual controller. In a force-reflecting teleoperation system, the operator uses the manual controller to direct the remote manipulator and receives visual information from a video image and/or graphical animation on the computer screen. This thesis presents the design of a portable Force-Reflecting Manual Controller (FRMC) for the teleoperation of tasks such as hazardous material handling, waste cleanup, and space-related operations. The work consists of the design and construction of a prototype 1-Degree-of-Freedom (DOF) FRMC, the development of the Graphical User Interface (GUI), and system integration. Two control strategies - PID and fuzzy logic controllers are developed and experimentally tested. The system response of each is analyzed and evaluated. In addition, the concept of a telesensation system is introduced, and a variety of design alternatives of a 3-DOF FRMC are proposed for future development

Visual Servoing

The goal of this book is to introduce the visional application by excellent researchers in the world currently and offer the knowledge that can also be applied to another field widely. This book collects the main studies about machine vision currently in the world, and has a powerful persuasion in the applications employed in the machine vision. The contents, which demonstrate that the machine vision theory, are realized in different field. For the beginner, it is easy to understand the development in the vision servoing. For engineer, professor and researcher, they can study and learn the chapters, and then employ another application method

Advanced Feedback Linearization Control for Tiltrotor UAVs: Gait Plan, Controller Design, and Stability Analysis

Three challenges, however, can hinder the application of Feedback Linearization: over-intensive control signals, singular decoupling matrix, and saturation. Activating any of these three issues can challenge the stability proof. To solve these three challenges, first, this research proposed the drone gait plan. The gait plan was initially used to figure out the control problems in quadruped (four-legged) robots; applying this approach, accompanied by Feedback Linearization, the quality of the control signals was enhanced. Then, we proposed the concept of unacceptable attitude curves, which are not allowed for the tiltrotor to travel to. The Two Color Map Theorem was subsequently established to enlarge the supported attitude for the tiltrotor. These theories were employed in the tiltrotor tracking problem with different references. Notable improvements in the control signals were witnessed in the tiltrotor simulator. Finally, we explored the control theory, the stability proof of the novel mobile robot (tilt vehicle) stabilized by Feedback Linearization with saturation. Instead of adopting the tiltrotor model, which is over-complicated, we designed a conceptual mobile robot (tilt-car) to analyze the stability proof. The stability proof (stable in the sense of Lyapunov) was found for a mobile robot (tilt vehicle) controlled by Feedback Linearization with saturation for the first time. The success tracking result with the promising control signals in the tiltrotor simulator demonstrates the advances of our control method. Also, the Lyapunov candidate and the tracking result in the mobile robot (tilt-car) simulator confirm our deductions of the stability proof. These results reveal that these three challenges in Feedback Linearization are solved, to some extents.Comment: Doctoral Thesis at The University of Toky

CONTROL STRATEGY OF MULTIROTOR PLATFORM UNDER NOMINAL AND FAULT CONDITIONS USING A DUAL-LOOP CONTROL SCHEME USED FOR EARTH-BASED SPACECRAFT CONTROL TESTING

Over the last decade, autonomous Unmanned Aerial Vehicles (UAVs) have seen increased usage in industrial, defense, research, and academic applications. Specific attention is given to multirotor platforms due to their high maneuverability, utility, and accessibility. As such, multirotors are often utilized in a variety of operating conditions such as populated areas, hazardous environments, inclement weather, etc. In this study, the effectiveness of multirotor platforms, specifically quadrotors, to behave as Earth-based satellite test platforms is discussed. Additionally, due to concerns over system operations under such circumstances, it becomes critical that multirotors are capable of operation despite experiencing undesired conditions and collisions which make the platform susceptible to on-board hardware faults. Without countermeasures to account for such faults, specifically actuator faults, a multirotors will experience catastrophic failure. In this thesis, a control strategy for a quadrotor under nominal and fault conditions is proposed. The process of defining the quadrotor dynamic model is discussed in detail. A dual-loop SMC/PID control scheme is proposed to control the attitude and position states of the nominal system. Actuator faults on-board the quadrotor are interpreted as motor performance losses, specifically loss in rotor speeds. To control a faulty system, an additive control scheme is implemented in conjunction with the nominal scheme. The quadrotor platform is developed via analysis of the various subcomponents. In addition, various physical parameters of the quadrotor are determined experimentally. Simulated and experimental testing showed promising results, and provide encouragement for further refinement in the future

Learning Autonomous Flight Controllers with Spiking Neural Networks

The ability of a robot to adapt in-mission to achieve an assigned goal is highly desirable. This thesis project places an emphasis on employing learning-based intelligent control methodologies to the development and implementation of an autonomous unmanned aerial vehicle (UAV). Flight control is carried out by evolving spiking neural networks (SNNs) with Hebbian plasticity. The proposed implementation is capable of learning and self-adaptation to model variations and uncertainties when the controller learned in simulation is deployed on a physical platform. Controller development for small multicopters often relies on simulations as an intermediate step, providing cheap, parallelisable, observable and reproducible optimisation with no risk of damage to hardware. Although model-based approaches have been widely utilised in the process of development, loss of performance can be observed on the target platform due to simplification of system dynamics in simulation (e.g., aerodynamics, servo dynamics, sensor uncertainties). Ignorance of these effects in simulation can significantly deteriorate performance when the controller is deployed. Previous approaches often require mathematical or simulation models with a high level of accuracy which can be difficult to obtain. This thesis, on the other hand, attempts to cross the reality gap between a low-fidelity simulation and the real platform. This is done using synaptic plasticity to adapt the SNN controller evolved in simulation to the actual UAV dynamics. The primary contribution of this work is the implementation of a procedural methodology for SNN control that integrates bioinspired learning mechanisms with artificial evolution, with an SNN library package (i.e. eSpinn) developed by the author. Distinct from existing SNN simulators that mainly focus on large-scale neuron interactions and learning mechanisms from a neuroscience perspective, the eSpinn library draws particular attention to embedded implementations on hardware that is applicable for problems in the robotic domain. This C++ software package is not only able to support simulations in the MATLAB and Python environment, allowing rapid prototyping and validation in simulation; but also capable of seamless transition between simulation and deployment on the embedded platforms. This work implements a modified version of the NEAT neuroevolution algorithm and leverages the power of evolutionary computation to discover functional controller compositions and optimise plasticity mechanisms for online adaptation. With the eSpinn software package the development of spiking neurocontrollers for all degrees of freedom of the UAV is demonstrated in simulation. Plastic height control is carried out on a physical hexacopter platform. Through a set of experiments it is shown that the evolved plastic controller can maintain its functionality by self-adapting to model changes and uncertainties that take place after evolutionary training, and consequently exhibit better performance than its non-plastic counterpart