Davi Antônio dos Santos

A Bayesian solution to the multiple composite hypothesis testing for fault diagnosis in dynamic systems

This paper is concerned with model-based isolation and estimation of additive faults in discrete-time linear Gaussian systems. The isolation problem is stated as a multiple composite hypothesis testing on the innovation sequence of the Kalman filter (KF) that considers the system operating under fault-free conditions. Fault estimation is carried out, after isolating a fault mode, by using the Maximum a Posteriori (MAP) criterion. An explicit solution is presented for both fault isolation and estimation when the parameters of the fault modes are assumed to be realizations of specific random variables (RV).

Adaptive Control With Convex and Saturation Constraints

This paper applies retrospective cost adaptive control (RCAC) to command following in the presence of multivariable convex input saturation constraints. To account for the saturation constraint, we use convex optimization to minimize the quadratic retrospective cost function. The use of convex optimization bounds the magnitude of the retrospectively optimized input and thereby influences the controller update to satisfy the control bounds. This technique is applied to a tiltrotor with constraints on the total thrust magnitude and inclination of the rotor plane.Copyright

Experimental evaluation of HJB optimal controllers for the attitude dynamics of a multirotor aerial vehicle

The present paper is concerned with the design and experimental evaluation of optimal control laws for the nonlinear attitude dynamics of a multirotor aerial vehicle. Three design methods based on Hamilton-Jacobi-Bellman equation are taken into account. The first one is a linear control with guarantee of stability for nonlinear systems. The second and third are a nonlinear suboptimal control techniques. These techniques are based on an optimal control design approach that takes into account the nonlinearities present in the vehicle dynamics. The stability Proof of the closed-loop system is presented. The performance of the control system designed is evaluated via simulations and also via an experimental scheme using the Quanser 3-DOF Hover. The experiments show the effectiveness of the linear control method over the nonlinear strategy.

Attitude Determination of Multirotor Aerial Vehicles Using Camera Vector Measurements

The employment of embedded cameras in navigation and guidance of Unmanned Aerial Vehicles (UAV) has attracted the focus of many academic researches. In particular, for the multirotor UAV, the camera is widely employed for applications performed in indoor environments, where the GNSS signal is often unreliable and electromagnetic interference can be a concern. In the literature, images are mostly adopted for position and velocity estimation, rather than attitude estimation. This paper proposes an attitude determination method for multirotor aerial vehicles using pairs of vector measurements taken from a downward-facing strapdown camera. The method is composed of three modules. The first one detects and identifies the visible landmarks by processing the images. The second module computes the vector measurements related to the direction from the camera to the landmarks. The third module estimates attitude from the vector measurements. In the last module, a version of the Multiplicative Extended Kalman Filter (MEKF) with sequential update is proposed as estimation method. The overall method is evaluated via Monte Carlo simulations, showing that it is effective in determining the vehicle’s attitude and revealing its properties.

Visual-Inertial Request For Attitude Determination Of Multirotor Aerial Vehicles

The present paper proposes a visual-inertial attitude estimator for multirotor aerial vehicles (MAV). The vehicle is assumed to be equipped with a 3-axis rate-gyro and a downward-pointing camera, both rigidly fixed on its structure. The method is based on the REQUEST algorithm, which is a well-known solution to the problem of attitude determination form vector measurements. Here the camera provides the vector measurements defined as unit vectors pointing from the camera optical center to known landmarks. The method is evaluated via Monte Carlo simulations which shows its perfomance for different number of visible landmarks and different values of a fadding factor parameter of the method.

Hovering Analysis of a Tethered Multirotor under External Disturbances

CON-2016-1210 Abstract: Multirotor Aerial Vehicles (MAVs) have been the subject of many academic studies and have attracted a lot of attention from industry in recent years. MAVs have flight capabilities such as hovering, Vertical Take-Off and Landing (VTOL) and agile maneuvering capability, which cannot be achieved by conventional fixed wing aircraft. However, such vehicles have limited autonomy, which results in flights of at most some minutes. A sub-category of aerial vehicles is tethered MAVs, which are anchored at a fixed point by a cable. While this limits their motion, it can also works as a power line, providing electrical power to the vehicle and enhancing its flight autonomy. This paper presents a modeling and hovering control strategy for tethered MAV. The vehicle is an octocopter, with flat configuration. A viscoelastic model is considered for the cable, in order to reproduce its dynamic behavior. The cable consists of a spring and a damper in parallel. The controller is based on a saturated state feedback control, thus simplifying the controller. The model was evaluated through numerical simulations using MATLAB/Simulink. The vehicle performed a hover flight and was subjected to external disturbances in order to emulate ambient wind. The results for the tethered MAV were compared with the MAV flying freely without cable. The tethered MAV presents improved hovering capability when compared with the vehicle without cable, due to the tension exerted by the cable, which provides a better robustness to exogenous perturbations.

Polynomial Chaos-based analysis of Multirotor Aerial Vehicles Model Subjected to Uncertainties

Abstract: Uncertainties are ubiquitous in mathematical equations that represent physical models and may come from unknown plant parameters or from the purposeful choice of a simplified representation of the system dynamics. In case of Multirotor Aerial Vehicles (MAVs), which have become interesting for applications where manned operations are considered inefficient and dangerous for humans, uncertainties such as inaccurate parameters, neglect of gyroscopic effect, blade flapping, as well as, wind gusts can have strong adverse effects on the system behavior. This paper uses the Polynomial Chaos Expansion method to propagate uncertainties in the multirotor model and characterize the effects over the vehicle trajectory, considering constraints on both the total thrust magnitude and the inclination of the rotor plane in the design of the position control. The Polynomial Chaos method constructs meta-models that accurately mimic the behavior of systems with uncertainty about the mean of stochastic inputs. The uncertainties are described in terms of normal distribution. The results are compared with Monte Carlo simulations.

Experimental Validation of a Trajectory Tracking Control using the AR.Drone Quadrotor

Abstract: In this paper, we describe a hardware-in-the-loop (HIL) architecture to validate a position control for a multirotor helicopter in indoor environment. For the implementation of the proposed architecture, it is used two central computers: one dedicated to design the execution of the position control, and the another is dedicated to determine the localization of the vehicle. The proposed system separate the whole position control into an altitude and a horizontal control. To estimate the horizontal position and velocity of the multirotor, we use a static Kinect for Windows sensor fixed on the ceiling and pointing downwards, while the altitude estimation is provided by the ultrasonic sensor embedded in the vehicle. Experimental results using the low-cost quadrotor AR.Drone 2.0 validate the position control along a circular trajectory as well as a hovering flight test subject to disturbances is evaluated. Palavras-chave: Aerial Robotics, AR.Drone 2, Position Control

Formation flight control of multirotor helicopters with collision avoidance

Among the main sub-areas covering the cooperative control problem of Unmanned Aerial Vehicles (UAVs), formation flight has attracted great interest and has been widely investigated. The main purpose of the formation flight control is to establish a desired shape of formation for a group of vehicles by controlling the positions of each vehicle. The present paper deals with the problem of position formation flight control of a group of three multirotor helicopters with collision avoidance. In order to solve the problem, we propose a decentralized scheme based on model predictive controllers (MPC) for formation according to a virtual structure approach. For collision avoidance, a set of convex constraints on the vehicles positions are included. The proposed method is evaluated on the basis of computational simulations considering that the vehicles are subject to disturbance forces. Simulation results show the effectiveness of the method with primary focus on treatment of anti-collision constraints.

Modeling and Control of Tethered Unmanned Multicopters in Hovering Flight

The dynamic modeling, navigation and control for free-flying Robots are greatly discussed topics in the scientific community. On the other hand, implications on tethered flights are rarely approached. This paper presents a modeling and control strategy for tethered unmanned multicopters. A viscoelastic model is considered for the cable, in order to reproduce its dynamic behavior. A control strategy is presented for hovering tethered multicopter and a comparison between free and tethered flight using an unmanned multicopter is presented.