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Control Systems

Design, test, and implement control systems

As a control systems engineer, you can use MATLAB® and Simulink® at all stages of development, including plant modeling, controller design, deployment with automatic code generation, and system verification. Using MATLAB and Simulink control systems products, you can:

  • Model linear and nonlinear plant dynamics using basic models, system identification, or automatic parameter estimation.

  • Trim, linearize, and compute frequency response for nonlinear Simulink models.

  • Design controllers based on plant models using root locus, Bode diagrams, LQR, LQG, and other design techniques.

  • Interactively analyze control system performance using overshoot, rise time, phase margin, gain margin, and other performance and stability characteristics in time and frequency domains.

  • Automatically tune PID, gain-scheduled, and arbitrary SISO and MIMO control systems.

  • Design and implement robust and model predictive controllers or use model-free control methods such as model-reference adaptive control, extremum-seeking control, reinforcement learning, and fuzzy logic.

  • Deploy control algorithms to embedded system for real-time control, tuning, or parameter estimation.

  • Design and test condition monitoring and predictive maintenance algorithms.

Topics

Plant Modeling, System Identification, and Parameter Estimation

Trimming, Linearization, and Frequency Response Estimation

Control Design and Tuning

Predictive and Robust Control

  • Design MPC Controller in Simulink (Model Predictive Control Toolbox)
    Design and simulate a model predictive controller for a Simulink model using MPC Designer.
  • Robust Control of Active Suspension (Robust Control Toolbox)
    In this example, use H synthesis to design a controller for a nominal plant model. Then, use μ synthesis to design a robust controller that accounts for uncertainty in the model.

Adaptive and Intelligent Control

Deployable Algorithms

Featured Examples