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Trajectory tracking is a classic control theory topic that has received in-depth research in the literature. However, dealing with compliant arms that is underactuated makes the issue more difficult. Compliant systems frequently exhibit difficult-to-model dynamics in addition to their underactuation. To prevent a severe modification of the robot elasticity, the feedback components should be limited. In this letter, we use an iterative learning controller to solve the trajectory tracking problem. The presented control law mixes feedforward and feedback terms. The feedforward component tracks the desired trajectory raising the robot to one equilibrium, and the feedback term stabilizes the equilibrium. We investigate the closed-loop stiffness variation. Finally, we simulate an underactuated compliant arm to verify the suggested technique.

Underactuated compliant arms are a promising technology to outperform traditional rigid robots in interaction tasks, thanks to their adaptability and shock-absorbing prop- erties. However, the control problem is yet to be solved. In this paper, we study the controllability for a class of planar underactuated compliant arms under gravity. We prove the Kalman rank condition for a system with one actuated elastic joint, i.e., base joint, and a generic number of unactuated elastic ones. The proof relies on the induction principle on the dimension of the reachability matrix. Finally, we generalize the result for a generic number of actuated elastic joints in the chain.

Interactions between robots and the environment frequently occur during most modern robotic applications. In- dependently from the nature - intentional or unintentional - of these interactions, they must be properly considered during robot design and control, otherwise, they may lead to dangerous and harmful scenarios, both for robots and for surrounding people. From the design side, a typical solution is soft robotics, i.e., the addition of elastic elements into the robot structure. However, the compliant robot behavior conferred by the soft elements could be drastically altered by the the adopted control technique. In this paper, we propose a control framework based on Iterative Learning Control for articulated soft robots, which is able to cope with the complex dynamics of these systems, while achieving good tracking performance. The preservation of the robot compliance is obtained through constraints on the feedback components. The main limitations of learning approaches are the issues related to generalization and scalability. For this reason, we tackle this problem by proposing solutions to generalize the learned control action w.r.t. stiffness profile, velocity execution, and trajectory.

In essence, airport slots are planning tools for the rationing of capacity at airports where available capacity falls short of air travel demand. Since the availability of slots is directly connected to the capacity of an airport at a particular date and time, a slot is by definition a scarce resource. The extent of. Slot scarcity depends on the congestion level of an airport. Excess demand for slots has substantial implications for airlines, coordinators and airports alike, as well as for society as a whole. This dissertation is designed to explore the compatibility of the global and specific legal regimes governing airport slot coordination with the particular socio-economic challenges that international organizations, governments and air transport industry stakeholders are experiencing today. A multitude of socio-economic objectives are identified, including but not limited to the environment in terms of noise and carbon reduction policies, growing airport access issues and general debates on airport functions to society. The number of so-called 'super-congested' airports in terms of the full slot capacity being historically 'occupied' by incumbent carriers are on the rise and are carefully studied from a policy and legal point of view.