Found 19 relevant results in 2.87s where lecturer="John Lygeros"
Real-world control problems are complex and safety‑critical, so PID control often needs more advanced methods. This module covers modern techniques, starting with optimal control, which uses optimization to boost performance and reduce costs. We introduce MPC for fast, constrained control, and explore multiagent systems to manage large‑scale interactions efficiently.
Introduction to basic concepts from automatic control theory and their application to the control and automation of buildings.
The CAS ETH in Automation offers an overview of the role of control and automation in modern technologies, infrastructures, and engineering systems. The focus is on the theories, methods, applications, state-of-the-art, and current trends that characterize the field of control engineering.
Beyond complexity, uncertainty is a key challenge. Models may be inaccurate, sensor data noisy, or actuator responses delayed. To address these issues, this module introduces essential tools such as system identification, state estimation, and robust control—methods that help maintain reliable performance despite real‑world imperfections.
Laboratory I
Fachpraktikum I
The Laboratory courses in the 5th and 6th semesters enable the students to put the the contents of the courses from the four first semesters to the test and to consolidate the aquired knowledge. Furthermore students have the possibilty to gain specific knowledge in certain software packages as MATLAB.
Laboratory II
Fachpraktikum II
The Laboratory courses in the 5th and 6th semesters enable the students to put the the contents of the courses from the four first semesters to the test and to consolidate the aquired knowledge. Furthermore students have the possibilty to gain specific knowledge in certain software packages as MATLAB.
This module introduces systems theory and automatic control. It outlines the historical and modern role of control theory and highlights its hidden importance in today’s technologies. Key topics include the basic control loop, the role of feedback, and using differential equations to model physical systems.
A major recent shift in control has been the rise of data‑driven and reinforcement learning methods. With data now central across industries, we show how to use it to design controllers that optimize performance and ensure safety. This module introduces key reinforcement learning techniques and the emerging theory of data‑enabled predictive control.
The class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work.
Dynamical systems are mathematical models used to describe reality, from robots to diseases to human decisions. This module covers key concepts such as solutions, equilibria, stability, controllability, and observability—core properties that form the basis for designing controllers that automatically regulate system behavior.
Course based on individual study, please contact instructor for more information. Short projects involving literature review and possibly simple research tasks.
Signal and System Theory II
Signal- und Systemtheorie II
Continuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability.
Signals and Systems II
Signal- und Systemtheorie II
Continuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability.
In this module we move from analysis to control. A controller uses sensor data to generate corrective actions through actuators, ensuring the system behaves as desired. We introduce open‑loop vs. closed‑loop control and explain the core ideas behind the Proportional, Integral, and Derivative actions of a PID controller.