Identification and Control of Position-Dependent High Precision Systems
High-performance control of systems with position-dependent dynamics is insufficiently developed for most next-generation manufacturing systems. This project aims to develop the necessary controller synthesis and identification tools to enable the desired performance through control.
In the traditional motion control approach, the system under consideration is approximated as a rigid body. However, increasing demands on throughput and accuracy of manufacturing systems lead a situation where the rigid body approximation is no longer valid and the flexible dynamics need to explicitly addressed. These dynamics lead to inherently multivariable and position-dependent behavior. As a consequence, a dynamic relation now exists between the measured positions and the points at which performance is required. These effects, which are not considered in the traditional LTI motion control approach need to be explicitly addressed to ensure satisfactory performance of these systems. Therefore, the aim of this project is to extend proven control techniques to enable high performance for position-dependent mechatronic systems. Especially learning- and optimal control techniques are expected to be the key ingredients for high performance. Since model knowledge of the system is imperative to controlling unmeasured variables, additional attention is paid to the development of system identification techniques for these position-dependent systems.