Major delivery companies such as DHL, UPS or Amazon have been developing small drones to deliver packages. This alternative to truck delivery is expected to start operating in the near future. The advantages of it are its speed, price, safety and sustainability: parcels would not be subjected to traffic and they would be delivered within an hour, it is 10% less expensive and it means a 73% reduction in CO2 emissions when compared to truck delivery, as well as a relieve in the road traffic network. The only drawback is that the noise produced by current drones is deemed by humans as more annoying than car noise due to its high frequency. The mission of the Silent Delivery Drone project is to provide a drone delivery system that is faster, less expensive and has lower emissions than truck delivery while complying with Dutch noise regulations. The presented innovative configuration is a combination of a quadcopter, suitable for Vertically Taking-Off and Landing (VTOL) in densely populated regions, and a flying wing, optimized for the cruise phase. It consists of a horizontal propeller used during cruise and four vertical propellers for VTOL. The drone can carry a payload of up to 2.5 kg, which corresponds to 89% of the packages delivered yearly worldwide. Four packages can be delivered while flying the maximum range of 30 km. Thanks to the low required revolutions per minute, the absolute maximum noise caused by the drone is 58 dBA at take-off from 7.5 m and 25 dBA during cruise from a distance of 120 m. This meets the Dutch night noise regulations which stablish a peak noise level of 65 dBA and average noise level of 40 dBA. We believe that a fleet of Silentium drones would revolutionize the way we perceive package delivery and it would mark the next step towards a greener, smarter and more connected future.

Improved understanding of human adaptation can be used to design better autonomous systems and control systems that can support the human controller when the dynamics of the system that is being controlled suddenly change. This paper evaluates the effectiveness of a model-based adaptive control technique, Model-Based Reference Control (MRAC), for predicting the adaptive control policy shown by human operators while controlling a time-varying system in a pursuit-tracking task. Ten participants took part in an experiment, where they were asked to control a time-varying system whose dynamics changed twice and approximated a single and double integrator dynamics. A MRAC controller is composed of a feedforward and a feedback controller and an internal model that is used to drive the adaptive control policy. The active gains, the internal model parameters and the learning rates, have been estimated via an non-linear optimization aimed at maximizing quality of fitting of the participants' control output. The participant's control behavior rapidly changed when the dynamics of the controlled system changed, in particular when going from controlling a first to second order system. The MRAC model was able to accurately capture the transient dynamics exhibited by the participants when the system changed approximately from a first to a double integrator while it failed to do so when the system changed from double to first integrator. In the latter case the MRAC gains changed too slowly. Therefore MRAC can be used to approximate human adaptations in pursuit tracking tasks when a change in the dynamics of the controlled system requires an increase in the rate feedback controller to ensure accurate tracking of the reference signal.