Data-Driven Control with MATLAB and Simulink

Design and implement data-driven and AI-based control algorithms

Engineers use data-driven control algorithms in scenarios where traditional control methods may fall short. These scenarios may occur when modeling plant dynamics using first principles is difficult or impractical, or when adaptive control is necessary.

With MATLAB and Simulink, you can:

  • Design, simulate, and implement data-driven control techniques using AI and non-AI-based methods
  • Identify system dynamics or learn controller parameters directly from data using offline techniques on your desktop
  • Update controller parameters in real-time within embedded systems using online techniques
  Offline Techniques Online Techniques
  • Model predictive control (MPC) with neural state-space
  • Offline reinforcement learning
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  • Online reinforcement learning
  • Model reference adaptive control with AI-based disturbance model
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  • Traditional methods with system identification
  • Fuzzy inference system (FIS) tuning
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  • Active disturbance rejection control
  • Model reference adaptive control with non-AI-based disturbance model
  • Extremum seeking control
  • Closed-Loop PID Autotuner
  • Adaptive MPC with online system identification
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Learn about the products that support AI and data-driven control techniques for control system design applications.