State Space
This series introduces control techniques built on state-space equations, the model representation of choice for modern control.
We will provide some intuition around how to think about state variables and why this representation is so powerful. We’ll walk through a simple but effective feedback controller called pole placement, or full state feedback, and show how it is able to move the eigenvalues of your system.
We’ll also describe the concepts of controllability and observability. Finally, we’ll look at the Linear Quadratic Regulator (LQR), a popular MIMO control technique, and show how you can use it to find optimal eigenvalue locations based on weighting criteria.
Introduction to State-Space Equations
Let’s introduce the state-space equations, the model representation of choice for modern control. This video will provide some intuition around how to think about state variables and why this representation is so powerful.
This video provides an intuitive understanding of pole placement, also known as full state feedback. This is a control technique that feeds back every state to guarantee closed-loop stability and is the stepping stone to other methods like LQR.
A Conceptual Approach to Controllability and Observability
This video helps you answer two really important questions that come up in control systems engineering: Is your system controllable? And is it observable? In this video, we’re going to approach the answers from a conceptual and intuitive direction.
LQR is a type of optimal control based on state-space representation.
Why the Riccati Equation Is important for LQR Control
This Tech Talk looks at an optimal controller called linear quadratic regulator, or LQR, and shows why the Riccati equation plays such an important role in solving it efficiently.
