Igor Afonso Acampora Prado

A safe position tracking strategy for multirotor helicopters

The interest for multirotor unmanned aerial vehicles (UAVs) is currently growing due to their low cost, high maneuverability, simplified mechanics, capability to perform vertical take-off and landing as well as hovering flight. These characteristics make them a promising technology suitable for applications such as surveillance of indoor environments. The present work faces the problem of safely controlling the position trajectory of multirotor UAVs by taking into consideration a conic constraint on the total thrust vector and a linear convex constraint on the position vector. The problem is solved using a linear state-space model predictive control (MPC) strategy, whose optimization is made handy by replacing the original conic constraint set on the thrust vector by an inscribed pyramidal space, which renders a linear set of inequalities. The control vector computed by the MPC is converted into a throttle command and an attitude command. The proposed method is evaluated on the basis of Monte-Carlo simulations taking into account a random disturbance force. The simulations show the effectiveness of the method in tracking the commanded trajectory while respecting the control and position constraints. They also predict the effect of both the commanded speed and the maximum inclination constraint on the system performance.

On Nonlinear Dynamics and Flight Control at High Angles of Attack With Uncertain Aerodynamics

In this paper we consider the flight dynamics of fighter aircraft at high angles of attack with uncertain aerodynamic coefficients. Stochastic parametric uncertainty is dealt with by employing spectral decomposition of the random variables by means of the generalized polynomial chaos expansion. We propose an optimal linear feedback strategy for the automatic pilot system to recover the aircraft from stall and provide acceptable dynamic response. Optimality of the proposed control law is proved by solving the Hamilton-Jacobi-Bellman equation and asymptotically stability of the controlled nonlinear aircraft model is guaranteed in the Lyapunov sense. Numerical results are verified with Monte-Carlo simulations.Copyright

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.

Simulation scheme for quadricopter control with labview and x-plane

Unmanned Aerial Vehicles (UAV) became interesting for applications where manned operations are considered inefficient and dangerous for humans. This paper is dedicated to one special type of UAV called quadricopter. Lately, this UAV have been in the spotlight, because it has the vertical take-off and landing feature, it can perform hovered flight with changes orientation capability, besides it has high maneuverability and simple mechanic. This paper proposes an virtual simulation environment for validate control techniques applied to quadricopters. This scheme will integrate the virtual environment of X-Plane flight simulator with the mathematical and graphical LabView tooling.

Software in the loop simulation for multirotor position tracking using a linear constrained model predictive controller

The multirotor unmanned aerial vehicles (UAVs) are experiencing a currently growing interest due to their high maneuverability, simplified mechanics and vertical takeoff and landing capability. Also for these reasons, the multirotor UAVs are suitable test-bed platforms for different position tracking control techniques. This paper solves the problem of safely controlling the position tracking of a multirotor UAV by using a linear state-space model predictive controller (MPC) formulation. The optimization is performed by replacing the original conic constraint set by a circumscribed pyramidal space that renders a linear set of inequalities on the total thrust vector. In order to improve the design eficiency of such kind of control systems, the present work uses a low-cost software-in-the-loop (SIL) simulation scheme for performance evaluation of control law for quadricopter. The scheme consists of two computers interconnected by an Ethernet network using the User Datagram Protocol (UDP). Basically, one computer runs MATLAB/Simulink while the other runs X-Plane. The control law is implemented in Simulink, whereas the vehicle dynamics as well as the flight environment are simulated in X-Plane.