The dynamics of fluid energy machines are often highly nonlinear and can change significantly over time. These nonlinearities are also observed in the operation of the Slip Length Tribometer (SLT), especially when the gap height is in the micrometre range. As the gap height approaches the micrometre scale, capillary effects become significant, introducing nonlinearities that complicate the control of the hydraulic system. A robust method for controlling a nonlinear system can be achieved by intelligent control, including fuzzy control, expert PID control, and neural networks, because neural network and fuzzy system can model any (sufficiently smooth) continuous nonlinear function in a compact set and the modelling error is becoming smaller.

Tasks

The goal of this work is to develop an intelligent control method for the SLT at the micrometre scale, aiming to achieve robust control over the operating range and effectively manage the nonlinearities introduced by capillary effects. The task can be divided into following subtasks:

• Investigate and implement an intelligent control strategy (e.g., fuzzy logic, expert PID control, neural networks) tailored to the SLT system's nonlinearities.
• Integrate dynamic PID scheduling into the existing LabVIEW program and collect data for performance analysis.
• Analyse the results and demonstrate how intelligent control improves PID performance metrics, such as error reduction, response time, and uncertainty minimization.

Preparations

• Interest in fluid mechanics and experimental work. (Experience with LabVIEW is a plus).
• Interest in control theory and programming.

What do we offer?

• In-depth insights into the research topic with hands-on experience in experimental setups and data analysis.
• Training in uncertainty propagation and best practices for Research Data Management (RDM).
• Close involvement in the research team of SLT.