Found 5 relevant results in 2.66s where lecturer="Esfandiar Shafai"

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151-0591-00L 2003W , 2004W , 2005W , 2006W , 2007W , 2008W , 2020W , 2021W , 2022W , 2023W , 2024W , 2025W , 2026W 4 Credits BSC D-MAVT , D-INFK

Analysis and synthesis for linear time-invariant control systems with one input and one output signal (SISO). State-space models, time response, stability conditions. Transfer functions and frequency response. Stability analysis under feedback: Root Locus, Bode plots, Nyquist condition. Feedback control synthesis: time- and frequency-domain specifications, PID lead/lag compensation, loop shaping.

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151-0590-00L 2005S , 2006S , 2007S , 2008S , 2020S , 2021S , 2022S , 2023S , 2024S , 2025S , 2026S 4 Credits BSC D-MAVT

This course builds upon the modeling and control of LTI SISO systems introduced in Control Systems I. It extends these foundations with state feedback and estimation, multi-input multi-output (MIMO) systems, nonlinear control, optimization, optimal control and model predictive control (MPC) for constrained linear systems.

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151-0592-00L 2004S 3 Credits

PID control: tuning of PID controllers; loop-shaping with a PID controller. Smith predictor. Open-loop and closed-loop control schemes. Model-based control with state space methods. The LQG/LTR method for the design of robust output feedback control. Applications and exercises using MATLAB and SIMULINK.

Engineering Tool: Introduction to MATLAB

Ingenieur-Tool: Einführung in MATLAB

151-0021-00L 2003W , 2004W , 2005W , 2006W , 2007W , 2008W , 2020S , 2020W , 2021W , 2022W 0.4 Credits BSC D-MAVT

Introduction to MATLAB; vectors and matrices; graphics in MATLAB; calculus, differential equations; programming with MATLAB; data analysis and statistics; interpolation and polynomials. Excercises with solutions: using MATLAB commands, technical applications.

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151-0557-00L 2005W 4 Credits

An important task within the area of system modeling is the identification of the parameters of a mathematical model of a system based on the signals observed at the input and the output of the system. This task is especially difficult for noisy signals. This lecture introduces the most important methods for identifying the model paramters under these circumstances.