Industry 4.0

Manufacturing tasks are often repetitive, involving the same processing steps being repeated over and over to produce identical parts. This makes them well suited for data driven methods that exploit repetition, like the ones developed for autonomous racing. This project addresses deploying the data-assisted predictive control methods in manufacturing applications. The resulting optimisation problems are often of formidable complexity, making them challenging to solve in real time. This calls for efficient solution methods that inherit the stability and performance guarantees of the predictive control design even when the search for the optimal solution has to be terminated prematurely.

Cyberphysical systems are composed by subsystems linked through physical interconnections and communication channels enabling distributed control architectures. By leveraging recent results on optimal control, we will develop distributed algorithms for the data-driven generation of networked regulators adapted to the communication topology. Special attention will be devoted to the decentralization of the design process, so as to facilitate the addition and removal of subsystems in a plug-and-play fashion. As benchmark examples, we will consider energy systems and manufacturing processes, which are key applications in the NCCR.

We will investigate the theoretical and practical challenges of using energy harvesting to power nodes distributed control systems. Combining energy sources such as temperature and vibration with battery systems and wireless links enables the placement of sensor nodes where they are needed for the best data quality, regardless of the availability of wired power or communication. In the context of the NCCR, we will jointly investigate some of the open challenges in the design of autonomous, energy-neutral automation systems. In a second phase, we will investigate suitable demonstrators and applications such as wireless sensing and control in motor control and energy systems.

Recent results on data-based control exploit alternative parametrizations of linear and nonlinear systems, such as those provided by behavioral theory and deep neural networks. In this project we focus on Input/Output Behavioral (IOB) models that have been used for control by assuming noiseless measurement or deterministic bounded disturbances. The goal of the project is to study the relation between IOB-based control methods and the existing approaches for stochastic systems, including system identification and stochastic model predictive control. New controllers will be tested on the energy systems relevant for the NCCR.

Selective laser melting is an additive manufacturing technology that uses a laser to melt powdered metal into a solid structure based on a sliced 3D CAD model. Currently, such processes are only controlled by setting the process parameters and set points in advance, following multiple process optimisation trials. In this project, process parameters will be optimized with a data-driven closed loop control approach, based on combination of predictive and iterative learning control.

This project is focused on the development of a data-driven framework for self-maintenance and self-optimization for autonomous (robot-assisted) manufacturing. The framework will be characterized by three main components:

1. Sensing and data processing
2. Detection of faults and warnings using the extracted features 
3. Process control and optimization (maintaining safety, optimizing performance

The acquired process data will be used to optimise the manufacturing process by optimising trajectories of the assisting robots, and process parameters, to improve manufacturing productivity.

Our goal is to develop novel algorithms for risk-aware Bayesian optimization and demonstrate them in a case study for high-precision linear drives requiring position accuracy and stability with less than 5 nm deviation. For this system, as for many other applications, it is crucial to optimize expected performance and ensure reliable outcomes.  We will develop computationally efficient algorithms trading expected performance with the variance in the outcome, considering risk measures on the optimization constraints, and compiling thi acquisition functions into neural network policies.