This work presents a new approach to the guidance and control of marine craft via HEOL, i.e., a new way of combining flatness-based and model-free controllers. Its goal is to develop a general regulator for Unmanned Surface Vehicles (USV). To do so, the well-known USV maneuvering model is simplified into a nominal Hovercraft model which is flat. A flatness-based controller is derived for the simplified USV model and the loop is closed via an intelligent proportional-derivative (iPD) regulator. We thus associate the well-documented natural robustness of flatness-based control and adaptivity of iPDs. The controller is applied in simulation to two surface vessels, one meeting the simplifying hypotheses, the other one being a generic USV of the literature. It is shown to stabilize both systems even in the presence of unmodeled environmental disturbances.

Abstract-- This paper presents experiments for embedded cooperative distributed model predictive control applied to a team of hovercraft floating on an air hockey table. The hovercraft collectively solve a centralized optimal control problem in each sampling step via a stabilizing decentralized real-time iteration scheme using the alternating direction method of multipliers. The efficient implementation does not require a central coordinator, executes onboard the hovercraft, and facilitates sampling intervals in the millisecond range. Model Predictive Control (MPC) is promising for robotics, because it explicitly accounts for actuator and safety constraints, interlaces motion planning with feedback control, and is applicable to output regulation, trajectory tracking, and path following [1]. Distributed optimization and Distributed MPC (DMPC) target cyber-physical systems such as energy networks [6] on the robots is required.

The hovercraft concept is not a new one, having floated drivers across land, sand and water since the 1950s. But 70 years on, now petrol heads finally have a reason to celebrate the vehicle, because what has been billed as the world's first ever luxury sports hovercraft is set to go on sale for $100,000 (£73,400). It may not be as quick as a supercar, with a top speed of around 60mph and 120 miles of range at 40 mph, but it's the fastest amphibious vehicle yet. And unlike a Ferrari, McLaren or Bugatti, this exciting new roadster travels on a cushion of air seven inches off the ground, allowing drivers to whizz across water and giving new meaning to the term off-road vehicle. Designed by the firm Von Mercier, which is named after British engineer and founder Michael Mercier, the Arosa is said to'blend cutting-edge hovercraft and electric vehicle innovation'.

Nostradamus may have accurately predicted the French Revolution and the atomic bomb, but there are some things even this reputed clairvoyant could not have predicted – like hovercrafts and robotic chefs. Who could have possibly foreseen that the 21st century would see technology advance at such a rapid pace that what once seemed like sci-fi would become a reality? In the ever-changing technological landscape, it is important to stay on top of the latest tech news and developments in order to successfully adapt and fully embrace the future. In keeping with this theme, we look to the future to see what tech gadgets and tech trends 2017 will hold. PlayStation VR – Virtual reality has become all the rage in recent years, and the gaming industry seems to be the biggest contributor to this technology, incorporating it into games to provide enhanced gaming experiences like never before.

Design-based learning (DBL) has many affordances for promoting deep and lasting learning of both content and complex skills. However, careful orchestration and scaffolding are usually needed to achieve its full potential. In this paper, we describe our efforts at implementing a software suite to meet the cognitive and meta-cognitive needs of learners engaged in DBL. In Study 1, our software suite gave learners the opportunity to design in simulation, to run experiments to learn the effects of variables, and it scaffolded science explanation construction. Through our analysis of study 1 we identified both cognitive and metacognitive needs that the software did not provide for. To meet these additional requirements, we added an interactive science resource and a case library to the software to provide multi-representational content material, to facilitate exploration, and to invite metacognitive reflection needed to do well at learning through design. Learners recognized what they did not understand, took initiative to explore those science concepts, and applied them in novel ways. We present here our analysis of the kinds of metacognitive help learners need to productively learn from design activities and some ways of providing that help. Our conclusion is that cognitive aid without related metacognitive aid is insufficient in a DBL environment.