Main Content

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.


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