The aim of the envisioned research is to develop a framework for self-organization of an automated vehicular system on all operational levels, ranging from the vehicle level up to and including the logistical level. Here, the underlying paradigm is that vehicles are interconnected through a vehicular ad hoc wireless network (VANET), allowing them to optimize their collective behavior by means of a distributed implementation of control algorithms. Consequently, self-organization of an interconnected system, in this particular application consisting of road vehicles, is key to the envisioned project. Since the main objective is to efficiently and safely transport goods or people, a multi-layered consensus control (MLCC) framework must be designed to create a desired emergent behavior of the system as a whole, incorporating both analysis and synthesis. The application area involves automated vehicles on public roads as well as automatic guided vehicles (AGV’s) in closed areas. Both types of applications are characterized by state transitions that occur, e.g., when switching between platooning, merging of a vehicle into a platoon, giving right of way, or avoiding an obstacle. In particular for application to autonomous vehicles on public roads, resilience of the system is crucial due to the presence of manually driven vehicles, acting as an external disturbance to the system. In addition, system robustness also plays an important role for AGV’s to cope with malfunctioning vehicles or other system failures.

Eindhoven University of Technology (TU/e) control group spin-off SRM solutions BV i.o. (SRMS) aims to promote CO2 emission reduction of vehicles by offering highly innovative and efficient Switched Reluctance Machine (SRM) based solutions that enable Internal Combustion Engine (ICE) downsizing while, at the same time, solving the generally accepted poor drivability problem of the downsized engine-powered vehicles. SRMS?s solution is a fully electric, universally applicable, standalone supercharging device, capable of improving the drivability of any ICE-powered vehicle. This device, called the iBooster, is a product of state-of-the-art research in high-speed turbomachinery and control of electric SRMs, conducted during the past few years at the TU/e. For the automotive industry, the SRMs are seen as the future electric motor design technology, due to their inherently low mass-production costs. Being fully electric, the SRMS supercharger is not only easy to install but also designed to deliver instantaneous torque boost which is entirely software-customizable. These innovative properties make it ideal for an initial introduction in the automotive aftermarket, whereas its continued development will incrementally evolve the iBooster into a licensable product for the OEMs and Tier1 companies. According to the research performed by Bosch Mahle: ?The global aftermarket for turbochargers accounts for more than EUR 1 billion and will grow to approximately EUR 1.7 billion by 2018. More than 550 million turbochargers will have been fitted in vehicles across the globe by 2018, with 50 percent to be found on the roads of Europe?. Assuming an average retail price of €1.250, for each turbocharger, and anticipating an annual market growth of 20%, we expect 1M units sold in the year 2020. Based on 4000 units sold we expect a turnover of approximately 3 million euro in 2020.

The purpose of this seminar is to investigate a fundamentally new framework for controlling manufacturing machines and scientific instruments by exploiting and learning from data. This will lead to unparalleled performance for such high-tech systems, in which the state- of-the-art systems are presently still being controlled by traditional control philosophies that do not exploit the major opportunities of the abundance of data. Manufacturing machines and scientific instruments have a key role in our society. Wafer scanner technology is arguably the most important example in this respect, since Integrated Circuits (ICs) have led to ubiquitous computing power, leading to major developments in communication, medical equipment, transportation, etc. In fact, Moores law dictates a doubling of IC complexity every two years, which is enabled by progress in wafer scanner technology. This wafer scanner technology is developed primarily by industries in The Netherlands and Japan, and it is crucial that this advantage is reinforced. Positioning systems, or motion systems, are the key subsystems in wafer scanner technology that enable the manufacturing machines to function by positioning the ICs within the machine. Future machines must achieve high accuracy of 0.1 nm to allow for a doubling of IC complexity through miniaturization. At the same time, speed and acceleration up to 1 m/s and 100 m/s2 must be achieved to achieve high throughput and hence market viability of the machine and low cost of IC production for the end-user. Although major achievements have been made to follow Moores law already for decades, a major breakthrough in the control paradigm is foreseen to be essential to continue the exponential growth of Moores law. The aim of this seminar is to exploit the huge amount of sensors, actuators, and data in controlling high-tech mechatronic systems, such as wafer scanners, to the limits of performance. Indeed, the working hypothesis of this seminar is that everything in the systems behaviour that can be predicted can also be compensated for. However, this is by no means possible through traditional design philosophies that are still common in the current state-of-the-art systems. The goal is to bring together researchers to develop a new fundamental design framework for learning from data in complex mechatronic systems in view of new generations of future data-intensive mechatronic systems with unparalleled performance. On a longer horizon, the research will have a major impact on the development of radically new data-intensive mechatronic systems, where the use of data and control will be used to design radically different and lightweight systems. Indeed, a radically new view on mechatronic design, automatic control, and machine learning is foreseen, where new system designs will be combined with spatially distributed actuators that control spatiotemporal deformations, leading to a huge potential in speed and accuracy. These systems will be continuously monitored in real time through data and models, which constitute digital twins, to monitor their performance, identify faults, and predictive maintenance.

Home energy management is not just essential for saving costs but an empowering tool to drive global sustainability through smart individual actions as scheduling appliance use and electric vehicles charging. This project leverages model predictive control (MPC) technology to dynamically optimize such actions incorporating real-time data on energy generation, demands, prices, grid congestion, and users preferences. Researchers will develop new MPC paradigms with potential within individual homes and across neighborhoods, where coordination can amplify benefits. The project will explore groundbreaking ideas in distributed MPC, paving the way for its integration into next-generation residential energy hubs.