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
AI-Based
Model predictive control (MPC) with neural state-space
Compare and combine various data-driven and traditional control techniques
Design, simulate, and implement model reference adaptive control (MRAC), active disturbance rejection control (ADRC), reinforcement learning (RL), model predictive control (MPC), and other data-driven and traditional control methods within a single environment.
Implement and test data-driven control algorithms in Simulink using pre-built Simulink blocks. Automatically generate code from your control algorithm for direct deployment on embedded hardware.
Utilize documented references and examples for flight control, robotics, energy management, and other applications to implement data-driven control techniques without starting from scratch.
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