These are all of the MATLAB Tech Talk videos that I mention in the video "Everything you need to know about Control Theory".
The video hasn't posted yet but when it does I'll link to it here as well.
The Tech Talk video loosely follows the layout that I created for the Map of Control Theory. It's worth checking this out if you're looking for an overview of different control methods and techniques.
Like in the video, we start here with open loop or feedforward control.
A control system has two main goals: get the system to track a setpoint, and reject disturbances. Feedback control is pretty powerful for this, but this video shows how feedforward control...See More
An important concept for control systems is that the system is actually controllable! Later, when we talk about feedback control then the concept of observability also becomes important.
This video helps you answer two really important questions that come up in control systems engineering: Is your system controllable? And is it observable? Assuming you have a good linear...See More
To develop a good controller, we usually need a mathematical representation of the system. Here are three ways to build a model.
Tuning a PID controller requires that you have a representation of the system you’re trying to control. This could be the physical hardware or a mathematical representation of that hardware...See More
We can also develop a model with data and system identification. This video is the start of a series that goes into more depth on the topic.
Get an introduction to system identification that covers what it is and where it fits in the bigger picture. See how the combination of data-driven methods and physical intuition can improve...See More
The next 11 resources in this journey are all different closed loop controller techniques.
Chances are you’ve interacted with something that uses a form of this control law, even if you weren’t aware of it. That’s why it is worth learning a bit more about what this control law is...See More
Often, the best control system is the simplest. When the system you’re trying to control is highly nonlinear, this can lead to very complex controllers. This video continues our discussion...See More
This video walks through a controller design for an active suspension system. Actually, we design two controllers. For the first, we use H infinity synthesis to design a controller for a...See More
Get an introduction to extremum seeking control—an adaptive control method for finding an optimal control input or set of system parameters without needing a model of your system, static...See More
Use an adaptive control method called model reference adaptive control (MRAC). This controller can adapt in real time to variations and uncertainty in the system that is being controlled...See More
Learn about the benefits of using model predictive control (MPC). MPC can handle multi-input multi-output (MIMO) systems that have interactions between their inputs and outputs. Due to these...See More
Artificial intelligence, machine learning, deep neural networks. These are terms that can spark your imagination of a future where robots are thinking and evolving creatures.In this video...See More
After the developing the closed loop controller, we talked about developing a plan. This video covers two popular methods.
This video explores some of the ways that we can use a map like a binary occupancy grid for motion and path planning. We briefly cover what motion planning means and how we can use a graph...See More
In order to achieve closed loop control we have to feedback an estimate of system state. This is where the Kalman filter comes in!
Discover common uses of Kalman filters by walking through some examples. A Kalman filter is an optimal estimation algorithm used to estimate states of a system from indirect and uncertain...See More
There are many different types of statistical filters like the Kalman filter. This video describes another approach using the particle filter.
This video presents a high-level understanding of the particle filter and shows how it can be used in Monte Carlo localization to determine the pose of a mobile robot inside a building. We...See More
Analysis, simulation, and test was the last section we covered in the video. These last two resources touch on some of the techniques and methods we have at our disposal.
Explore three popular methods to visualize the frequency response of a linear time-invariant (LTI) system: the Nichols chart, the Nyquist plot, and the Bode plot. Learn about each method...See More