Control System Toolbox

Videos

  • Design and analyze control systems using Control System Toolbox™.
  • Design PID controllers using Control System Toolbox.
  • Design control systems with the Control System Designer app.
  • Identify a plant model from measured input-output data and use this model to tune PID Controller gains.
  • Design and implement a gain-scheduled PID controller for a continuous-stirred tank reactor using Simulink Control Design™.
  • Design and analyze a controller for different operating points of a nonlinear plant simultaneously.
  • Learn how to get started with using MATLAB ® and Simulink ® products for designing control systems.
  • Approximate nonlinear Simulink ® model with a low-order linear model.
  • Discover situations in which you’d use Kalman filters. Kalman filters are used to estimate the internal states of a system in the presence of uncertain and indirect measurements. Learn the principles...
  • Learn the basic concepts behind controls systems. Walk through everyday examples that outline fundamental ideas, and explore open-loop and feedback control systems.
  • Understand key aspects of Bode plots such as how frequency domain analysis helps you understand behavior of physical systems, the principal characteristics of a Bode plot, building Bode plots for first-order systems and building Bode plots for second- and higher-order systems.
  • Understand how to use Bode plots by learning desired frequency domain shapes for sensitivity and complementary sensitivity transfer functions, what gain margin and phase margins are and how to use them for control, frequency domain characteristics of lead, lag, and PID controllers, and how to use Bode plots for DC motor speed control.
  • This series provides an overview of reinforcement learning, a type of machine learning that has the potential to solve some control system problems that are too difficult to solve with traditional techniques. We’ll cover the basics of the reinforcement problem and how it differs from traditional control techniques. We’ll show why neural networks...
  • This series introduces control techniques built on state-space equations; the model representation of choice for modern control. We’ll discuss topics such as pole placement, full-state feedback, and Linear Quadratic Regulator.
  • Predictive maintenance lets you estimate the optimum time to do maintenance by predicting time to failure of a machine. In this series, you’ll learn about a workflow you can follow to develop a predictive maintenance algorithm. The videos use triplex pump and aircraft engine examples to walk you through the workflow steps such as feature...
  • This series introduces the concept of linearization and covers some of the topics that will help you understand how linearization is used and why it's helpful.
  • See a workflow for developing a control system that takes you from the basics of drone mechanics and to the test flight.
  • In this series, you’ll learn some of the more practical aspects of being a control systems engineer and designing control systems. This series covers topics such as gain scheduling, feedforward control, and time delays in dynamic systems.
  • Create and analyze state-space models using MATLAB ® and Control System Toolbox™. State-space models are commonly used for representing linear time-invariant (LTI) systems.
  • Design a full-state feedback controller using pole placement with Control System Toolbox™.
  • Work with transfer functions using MATLAB ® and Control System Toolbox™.
  • Create transfer functions in Simulink ® , and learn how they can be used to model and simulate complex systems.
  • Extract transfer functions from linear and nonlinear Simulink ® models using the Linear Analysis Tool.