Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Determination of local density of states in amorphous oxide semiconductors with Kelvin Probe Force Microscopy

Amorphous semiconductors are important materials as they can be deposited by physical deposition techniques on large areas and even on plastic substrates. Therefore, they are crucial for transistors in large active matrices for imaging and transparent wearable electronics. The most widely applied candidate for amorphous thin film transistors production is Indium Gallium Zinc Oxide (IGZO). It is attracting much interest because of its optical transparency, facile processing by sputtering deposition and notable improved charge carrier mobility with respect to hydrogenated amorphous silicon a-Si:H. Degradation of the device and long-term performance issues have been observed if IGZO thin film transistors are subjected to electrical stress, leading to a modification of IGZO channel properties and subthreshold slope. Therefore, it is of great interest to have a reliable and precise method to study the conduction band tail, and the density of states in amorphous semiconductors. The aim of this thesis is to develop a local technique using Kelvin Probe Force Microscopy to study the evolution of IGZO DOS properties. The work is divided into three main parts. First, solutions to the non-linear Poisson-Boltzmann equation of a metal-insulator-semiconductor junction describing the charge accumulation and its relation to DOS properties are elaborated. Second macroscopic techniques such as capacitance voltage (CV) measurements and photocurrent spectroscopy are applied to obtain a non-local estimate of band-tail DOS properties in thin film transistor samples. The third part of my my thesis is dedicated to the KPFM measurements. By fitting the data to the developed numerical model, important parameters describing the amorphous conduction band tail are obtained. The results are in excellent agreement with the macroscopic characterizations. KPFM result is comparable also with non-local optoelectronic characterizations, such as photocurrent spectroscopy.

Distributed collision-free traffic management via vertex cover for multi-vehicle systems

In this thesis, we state the collision avoidance problem as a vertex covering problem, then we consider a distributed framework in which a team of cooperating Unmanned Vehicles (UVs) aim to solve this optimization problem cooperatively to guarantee collision avoidance between group members. For this purpose, we implement a distributed control scheme based on a robust Set-Theoretic Model Predictive Control ( ST-MPC) strategy, where the problem involves vehicles with independent dynamics but with coupled constraints, to capture required cooperative behavior.