Modeling and System Identification of the muFly Micro Helicopter

An accurate mathematical model is indispensable for simulation and control of a micro helicopter. The nonlinear model in this work is based on the rigid body motion where all external forces and moments as well as the dynamics of the different hardware elements are discussed and derived in detail. The important model parameters are estimated, measured or identified in an identification process. While most parameters are identified from test bench measurements, the remaining ones are identified on subsystems using the linear prediction error method on real flight data. The good results allow to use the systems for the attitude and altitude controller desig

Intelligent control of miniature holonomic vertical take-off and landing robot

This paper discusses the development of a fuzzy based controller for miniaturized unmanned aerial vehicle (UAV).This controller is designed to control the center-of-gravity (CoG) in a new configuration of coaxial miniaturized flying robot (MFR). The idea is to shift the CoG by controlling two pendulums located in perpendicular directions; each pendulum ends with a small mass. A key feature of this work is that the control algorithm represents the original nonlinear function that describes the dynamics of the proposed system. The controller model incorporates two cascaded subsystems: PD and PI fuzzy logic controllers. These two controllers regulate the attitude and the position of the flying robot, respectively. A model of the proposed controllers has been developed and evaluated in terms of stability and maneuverability. The results show that the presented control system can be used efficiently for the MFR applications

Dynamic analysis and qft-based robust control design of a coaxial micro-helicopter

This paper presents the dynamic behavior of a coaxial micro-helicopter, under Quantitative Feedback Theory (QFT) control. The flight dynamics of autonomous air vehicles (AAVs) with rotating rings is non-linear and complex. Then, it becomes necessary to characterize these non-linearities for each flight configuration, in order to provide these autonomous air vehicles (AAVs) with autonomous flight and navigation capabilities. Then, the nonlinear model is linearized around the operating point using some assumptions. Finally, a robust QFT control law over the coaxial micro-helicopter is applied to meet some specifications. QFT (quantitative feedback theory) is a control law design method that uses frequency domain concepts to meet performance specifications while managing uncertainty. This method is based on the feedback control when the plant is uncertain or when uncertain disturbances are affecting the plant. The QFT design approach involves conventional frequency response loop shaping by manipulating the gain variable with the poles and zeros of the nominal transfer function. The design process is accomplished by using MATLAB environment software

Estudi de l'helicòpter coaxial UAV i disseny del controlador

En aquest document es presenta un estudi complet sobre l’helicòpter coaxial UAV, la seva dinàmica, i control automàtic. En primer lloc s’analitzen els factors que intervenen en la física de l’helicòpter, per a acabar presentant un model no lineal que descrigui el sistema. A partir de simulacions, es valida el model no lineal i es procedeix a linealitzar-lo, amb l’objectiu final de dissenyar un controlador automàtic per l’helicòpter. A continuació de l’estudi sobre els aspectes de control, es presenta el controlador, i es mostren els resultats obtinguts. A la part final de l’estudi es presenten les conclusions extretes. Aquest document, també inclou al final un càlcul del pressupost de l’estudi, un anàlisi mediambiental, i la bibliografia emprada

3D Motion Planning using Kinodynamically Feasible Motion Primitives in Unknown Environments

Autonomous vehicles are a great asset to society by helping perform many dangerous or tedious tasks. They have already been successfully employed for many practical applications, such as search and rescue, automated surveillance, exploration and mapping, sample collection, and remote inspection. In order to perform most tasks autonomously, the vehicle must be able to safely and efficiently navigate through its environment. The algorithms and techniques that allow an autonomous vehicle to find traversable paths to its destination defines the set of problems in robotics known as motion planning. This thesis presents a new motion planner that is capable of finding collision-free paths through an unknown environment while satisfying the kinodynamic constraints of the vehicle. This is done using a two step process. In the first step, a collision-free path is generated using a modified Probabilistic Roadmap (PRM) based planner by assuming unexplored areas are obstacle-free. As obstacles are detected, the planner will replan the path as necessary to ensure that it remains collision-free. In complex environments, it is often necessary to increase the size of the PRM graph during the replanning step so that the graph remains connected. However, this causes the algorithm to slow down significantly over time. To mitigate these issues, the novel local sampling and PRM regeneration techniques are used to increase the computational efficiency of the replanning step. The local sampling technique biases the search towards the neighborhood of the obstacle blocking the path. This encourages the planner to generate small detours around the obstacle instead of rerouting the whole path. The PRM regeneration technique is used to remove all non-critical nodes from the PRM graph. This is used to bound the size of the PRM graph so that it does not grow increasingly large over time. In the second step, the collision-free path is transformed into a series of kinodynamically feasible motion primitives using two novel algorithms: the heuristic re-sampling algorithm and the transformation algorithm. The heuristic re-sampling algorithm is a greedy heuristic algorithm that increases the clearance around the path while removing redundant segments. This algorithm can be applied to any piece-wise linear path, and is guaranteed to produce a solution that is at least as good as the initial path. The transformation algorithm is a method to convert a path into a series of kinodynamically feasible motion primitives. It is extremely efficient computationally, and can be applied to any piece-wise linear path. To achieve good computational performance with PRM based planners, it is necessary to use sampling strategies that can efficiently form connected graphs through narrow and complex regions of the configuration space. Many proposed sampling methods attempt to bias the sample density in favor of these difficult to connect areas. However, these methods do not distinguish between samples that lie inside narrow passages and those that lie along convex borders. The orthogonal bridge test is a novel sampling technique that can identify and reject samples that lie along convex borders. This allows connected PRM graphs to be constructed with fewer nodes, which leads to less collision checking and reduced runtimes. The presented algorithms are experimentally verified using an AR.Drone quadrotor unmanned aerial vehicle (UAV) and a custom built skid-steer unmanned ground vehicle (UGV). Using a simple kinematic model and a basic position controller, the AR.Drone is able to traverse a series of motion primitives with less than 0.3 m of crosstrack error. The skid-steer UGV is able to navigate through unknown environments filled with obstacles to reach a desired destination. Furthermore, the observed runtimes of the proposed motion planner suggest that it is fully capable of computing solution paths online. This is an important result, because online computation is necessary for efficient autonomous operations and it can not be achieved with many existing kinodynamic motion planners

Modeling and System Identification of the muFly Micro Helicopter

ISSN:0921-0296ISSN:1573-040