Modern control and state space representation

Ready to advance your control systems knowledge? This course is designed for students who have a solid foundation in classical control theory and are eager to delve deeper. You'll gain practical skills through comprehensive lectures and hands-on problem-solving tutorials.

Here's a breakdown of the key topics we'll cover:

• Modeling of Mass-Spring Systems: Learn to create mathematical models of physical systems, a fundamental skill in control engineering.
• Transfer Functions & Laplace Transform: Master the essential tools for analyzing and designing control systems. We'll explore their application in understanding system behavior.
• State-Space Representation: Discover a powerful alternative to transfer functions for representing dynamic systems, crucial for advanced control design.
• CCF & OCF (Common and Unique Characteristic Functions): Understand and apply these techniques for analyzing system stability and performance.
• Cascade & Parallel Realization: Explore different ways to connect system blocks and their impact on overall system characteristics.
• Controllability & Observability: Learn how to assess the feasibility of controlling and observing a system's state, vital for effective feedback control.
• State Feedback: Implement state feedback control strategies to achieve desired system performance.
• Output Feedback: Design output feedback controllers for robust and adaptable control.

We'll also include a thorough review of:

• Stability: Deepen your understanding of system stability criteria.
• Eigenvalues & Eigenvectors: Explore the role of eigenvalues and eigenvectors in determining system behavior and stability.

This course provides a comprehensive path to mastering advanced control techniques, building upon your existing classical control knowledge.