In the last decades, the growing use of low-cost electronics has pushed Unmanned Aerial Vehicles (UAVs) to general purpose audience in the civil sector. An accurate attitude and position estimation is crucial for a precise attitude and altitude control of this type of aircraft and represents a fundamental aspect to be taken into account. The main aim of this paper is to propose a Moving Horizon Estimator (MHE) able to fuse raw data coming from a set of sensors composed of an Inertial Measurement Unit (IMU), an optimal flow camera and several Time of Flight (ToF) distance sensors, oriented downward, with the scope of improving estimation performance in indoor or GPS-denied environment. The use of MHE allows to take into account constraints on state dynamics and noise features. To test the effectiveness of the proposed MHE algorithm, numerical simulations were conducted, evaluating the performance of the proposed algorithm in the presence of attitude maneuvers.

During recent decades, Unmanned Aerial Vehicles (UAVs) has increased their success in several areas of applications thanks to their versatility. Furthermore, the growing availability of low-cost electronics has pushed their use in civil consumer applications. Attitude control is one of the needed tasks to effectively carry out missions, whose accuracy and adaptability to high payload imbalances or atmospheric disturbances are fundamental requirements. This paper focuses on the design of a flight control scheme based on Model Reference Adaptive Control with Neural Networks. The effectiveness of the proposed controller is assessed through experimental tests carried out on a Crazyflie 2.1 quadrotor.

In the last decades, the growing use of small Unmanned Aerial Vehicles (UAVs) has resulted in an escalating demand for low-cost Inertial Measurement Units (IMUs), usually made of Micro Electro-Mechanical Systems (MEMS) to measure accelerations, angular velocities and, optionally, magnetic field components along three axes. An accurate calibration of these devices is needed for the precise determination of aircraft attitude. Although their versatile applications, MEMS-based IMUs exhibit a high level of noise, including both systematic and stochastic errors. Systematic errors need an appropriate calibration process to be identified and eliminated. This paper presents a portable low-cost IMU calibration platform to provide the parameters to mitigate the errors that characterize this kind of devices. Using three servomotors positioned to enable rotations around three orthogonal axes, the system allows for calibrating IMUs both statically and dynamically. To prove the effectiveness of the proposed platform, a performance evaluation is provided, showcasing the difference in estimating the attitude of an IMU, calibrated with and without the use of the platform.

In the rapidly evolving landscape of unmanned aerial vehicles (UAVs), the optimization of cooperative behaviors holds paramount importance. In the field of cooperative trajectory planning for formations of unmanned aerial vehicles (UAVs), this paper presents a novel approach that separates geometric path planning from collision avoidance. While the geometric path optimization considers a piecewise path made of straight segments, clothoid curves and circular arcs to avoid fixed obstacles, the collision avoidance between UAVs is solved through the definition of a mixed-integer quadratic programming optimal control problem.

This paper is focused on the design of a Model Predictive Control (MPC) algorithm for a micro-satellite with a mass of about 20 kg, equipped with an umbrella-like deployable front structure. This control device allows the vehicle to maneuver and track a prescribed trajectory during the re-entry phase by changing the aerobrake surface. The proposed MPC controller is aimed at minimizing the error between the desired target position at an altitude of about 30 km, after which the satellite follows an uncontrolled ballistic trajectory. A single control move is updated at a sampling rate of 0.1 Hz trying to balance performance with computational burden for a possible real time implementation. To prove that the proposed MPC strategy implies a limited loss of performance, a comparison with MPC controllers optimizing more than one control move has been carried out.

In order to test aircraft actuators moving aerodynamic control surfaces, it becomes more and more important to have flexible facilities which are able to produce a variety of loading conditions with a given accuracy. The so-called Modular Iron Bird (MIB), which is now in the design phase, will be able to reproduce aerodynamic and inertial loads for a large category of aircraft. The MIB control system has to guarantee the application of a desired time history of forces through a hydraulic system, while the aircraft actuator is driven in closed-loop to guarantee a given aerodynamic surface deflection. Therefore, two controllers are fighting each other, and this makes the control problem challenging. In this paper, a nonlinear model-based predictive control strategy is proposed. Numerical results allow to test the effectiveness of this control strategy and its benefit with respect to the classical PID approach.

This paper proposes the application of a hybrid consensus on information (CI) and consensus on measurements (CM) algorithm, named HCMCI, to deal with the Distributed State Estimation (DSE) problem for a set of cooperating UAVs equipped with heterogeneous on board sensors and limited range communication devices. The benefit of the proposed algorithm is to allow to each vehicle to estimate position and velocity of the entire set of vehicles using only its own on board sensors and the information received only from the adjacent vehicles. The meaning of the theoretical conditions required to guarantee the stability of the estimate are discussed, with respect to the particular application. Numerical simulation are presented of different flight scenarios meaningful for the UAS Traffic Management (UTM) context.

This paper deals with a distributed path planning and Collision Avoidance System (CAS) for multiple aircraft sharing the same airspace. The algorithm is compliant with aircraft limits on maximum turn rate and with environmental constraints resulting from no-fly zones and/or obstacles. The avoidance maneuver is calculated on the basis of the Essential Visibility Graph and Dubins curves. Numerical examples reproducing urban-like environments show the effectiveness of the proposed approach.