Systems of Tethered Multicopters: Modeling and Control Design

A class of tethered unmanned aerial vehicles is considered, featuring a chain of multicopter drones tethered one to the other. Differently from previous contributions in the literature, here the tethers are assumed to be elastic and to transfer traction loads only. Moreover, their length can be adjusted through controlled winches installed in the ground station and on each drone. Named systems of tethered multicopters, these devices can be used for a range of applications where both long runtime and good flexibility are required. The paper describes a model of the system, and presents a hierarchical control approach for this class of drones. The proposed control approach is tested through numerical simulations

Day-Ahead and Intra-Day Building Load Forecast With Uncertainty Bounds Using Small Data Batches

An approach to provide day-ahead and intra-day load forecasts of buildings, such as electrical or thermal power consumption, is presented. The method aims to obtain a nominal forecast and associated error bounds with small data batches of two weeks for the training phase, resulting in a ready-to-go algorithm that can be employed whenever large datasets of months or years are not available or manageable. These cases include new or renovated constructions, buildings that are subject to changes in purpose and occupants' behavior, or applications on local devices with memory limits. The approach relies on a so-called "fictitious input" signal to capture the prior information on seasonal and periodic trends of load consumption. Then, linear multistep predictors with different horizon lengths are trained periodically with a small batch of the most recent data, and the associated worst case error bounds are derived, using set membership (SM) methods. Finally, the forecast is computed, for each time step, by intersecting the error bounds of the different multistep predictions and taking the central value of the obtained interval. Such a method is applied here for the first time to real-world data of electrical power consumption of a medium-size building and of cooling power consumption of a large complex. In both cases, the obtained results indicate a tightening of the worst case error bounds between 15% and 25% on average with respect to those obtained with a standard linear SM approach

Multitrajectory Model Predictive Control for Safe UAV Navigation in an Unknown Environment

The problem of navigating an unmanned aerial vehicle (UAV) in an unknown environment is addressed with a novel model predictive control (MPC) formulation, named multitrajectory MPC (mt-MPC). The objective is to safely drive the vehicle to the desired target location by relying only on the partial description of the surroundings provided by an exteroceptive sensor. This information results in time-varying constraints during the navigation among obstacles. The proposed mt-MPC generates a sequence of position set points that are fed to control loops at lower hierarchical levels. To do so, the mt-MPC predicts two different state trajectories, a safe one and an exploiting one, in the same finite horizon optimal control problem (FHOCP). This formulation, particularly suitable for problems with uncertain time-varying constraints, allows one to partially decouple constraint satisfaction (safety) from cost function minimization (exploitation). Uncertainty due to modeling errors and sensors noise is taken into account as well, in a set membership (SM) framework. Theoretical guarantees of persistent obstacle avoidance are derived under suitable assumptions, and the approach is demonstrated experimentally out-of-the-laboratory on a prototype built with off-the-shelf components

Arc Simulation in Low Voltage Switching Devices, a Case Study

Arc simulations are becoming a valuable tool in the development of low voltage switching devices. Sim-ulations reveal physical quantities that are experimentally not accessible and help in the investigation of the underlying phenomena. However, the strong interaction between different processes and the intrinsic multi-scale nature of the problem, both in time and space, pose great challenges to accurate and efficient simulations. At ABB Corporate Research, we developed a simulation tool capable of simulating the be-havior of low voltage switchgear. To verify the accuracy and predictive capability of our platform, we validate the simulations by comparing their results with available experimental findings. After describing the tool, we provide here evidence of the good agreement between measured and simulated data on several commercial ABB devices