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A robust optimization approach has been applied to the design of a piezoelectric brake. The force generated by the piezoelectric actuator is transmitted to the pad shoe through a lever. The optimal design of the lever is crucial for obtaining the desired performance of the brake. Increasing the stiffness and reducing the mass of the lever is the key problem for such kind of mechatronic brake. A trade off between mass and stiffness of the lever must be obtained. Multi-objective programming (MOP) has been applied in order to achieve the best compromise. In addition to MOP, the optimal robust design method has been applied to perform the optimal design not only by considering the performance of the system (the stiffness and mass of the lever) but also by taking into account the robustness (the sensitivity to the uncertain system parameters).

Roundabouts are intersections at which automated cars seem currently not performing sufficiently well. Actually, sometimes, they get stuck and the traffic flow is seriously reduced. To overcome this problem a V2N-N2V (vehicle-to-network-network-to-vehicle) communication scheme is proposed. Cars communicate via 5G with an edge computer. A cooperative machine-learning algorithm orchestrates the traffic. Automated cars are instructed to accelerate or decelerate with the triple aim of improving the traffic flow into the roundabout, keeping safety constraints, and providing comfort for passengers on board of automated vehicles. In the roundabout, both automated cars and human-driven cars run. The roundabout scenario has been simulated by SUMO. Additionally, the scenario has been reconstructed into a dynamic driving simulator, with a real human driver in a virtual reality environment. The aim was to check the human perception of traffic flow, driving safety and driving comfort.