Die Anmeldung endete am 12.10.2026 um 23:59 Uhr.

This course explores the theoretical foundations and algorithmic approaches for identifying and controlling discrete-time dynamical systems. Emphasis is placed on the modeling, estimation, and control of linear and nonlinear systems using data-driven and model-based techniques. Starting from the concept of transfer function in Z-domain, the direct and idirect method to design a controller are considered. Nonlinear anylysis based on the consinuous and discrete sliding Mode Control are proposed. The course will also address challenges such as system uncertainty, limited observability, noise, and computational constraints. Key topics include system identification, optimal control, adaptive control, and learning-based methods.

By the end of the course, students will be able to:

Formulate discrete-time dynamical system models from physical processes and data.

Understand and implement classical and modern identification techniques.

Design controllers in discrete-time using model-based and data-driven methods: Consinuous and Discrete Classical Methods (Root Locus based Methods) Sliding Mode Control, Model Predictive Control and Dead Beat Control design.

Analyze identifiability and observability conditions.

Critically assess stability, robustness, and convergence in control algorithms.

Apply identification and control tools to real-world problems through simulation or practical implementation.

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