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Our research unit Complex Dynamical Systems at the Center for Vision, Automation & Control invites applications for a master thesis. We focus our research on modelling, simulation, analysis and optimization, as well as on the control of complex and dynamical industrial systems and processes. The primary goal is to enhance product quality, increase flexibility, and boost resource efficiency. Applications are found in the fields of mechatronics, robotics, and industrial production facilities.

• Literature review: A comprehensive review of known solution methods and available solvers for such problems shall be conducted.

• Solution algorithms: Incorporating an existing state-space model of the considered processing line, an optimal control problem involving continuous and discrete control inputs shall be defined and an algorithm for its solution shall be chosen or developed. Based on reasonable performance metrics, the algorithm is to be evaluated.

• Use-case: The solution algorithm considered is to be practically tested. The test should involve closed-loop simulations of an MPC tracking control strategy for the strip temperature. The implementation may be realized using Matlab.

• You will be trained in scientific work, and you will have the opportunity to discuss your experiences with scientific interdisciplinary experts from the fields of mathematics, informatics, physics and control and mechanical engineering.

In many industrial applications and control systems, decisions containing both continuous and nonlinear decision variables have to be made. This means that mixed-integer optimization problems must be solved. This work considers a processing line for continuous heat treatment of steel strips that operates in two modes: heating and cooling.

Specifically, this work aims at formulating an optimal control problem for strip temperature tracking in the considered processing line and at developing, implementing, and assessing a numerical solution method. An efficient, real-time-capable solution of this problem form the basis for the successful implementation of a model predictive strip temperature control (MPC).