Control System Toolbox provides algorithms and apps for systematically analyzing, designing, and tuning linear control systems. You can specify your system as a transfer function, state-space, zero-pole-gain, or frequency-response model. Apps and functions, such as step response plot and Bode plot, let you analyze and visualize system behavior in the time and frequency domains.
You can tune compensator parameters using interactive techniques such as Bode loop shaping and the root locus method. The toolbox automatically tunes both SISO and MIMO compensators, including PID controllers. Compensators can include multiple tunable blocks spanning several feedback loops. You can tune gain-scheduled controllers and specify multiple tuning objectives, such as reference tracking, disturbance rejection, and stability margins. You can validate your design by verifying rise time, overshoot, settling time, gain and phase margins, and other requirements.
Dynamic System Modeling
Create linear models of your control system as transfer functions, (sparse) state-space models, LPV and LTV models, and other representations. Discretize and resample models. Simplify analysis and control design by reducing model order.
Linear Analysis
Visualize system behavior in the time and frequency domain. Compute system characteristics such as rise time, overshoot, and settling time. Analyze system stability by computing gain and phase margins and crossover frequencies.
PID Tuning
Automatically tune PID controller gains to balance performance and robustness using the PID Tuner app or command-line functions. Tune continuous or discrete controllers and 2-DOF PID controllers.
Compensator Design
Interactively design and analyze single-input, single-output (SISO) controllers with the Control System Designer app, using automated tuning methods. Graphically tune common control components using root locus, Bode diagrams, and Nichols charts.
State Estimation and State-Space Control Design
Use state-space control design methods, such as LQR/LQG and pole-placement algorithms. Estimate system states using observers, including linear and nonlinear Kalman filters.
Multiloop, Multiobjective Tuning
Automatically tune arbitrary SISO and MIMO decentralized control structures modeled in MATLAB or Simulink to satisfy time and frequency-domain design requirements using the Control System Tuner app.
Gain Scheduling
Design gain-scheduled controllers for nonlinear or time-varying plants. Specify requirements and automatically tune gain surface coefficients. Validate the tuning results across the entire operating range of your design.
Control Design in Simulink
Analyze and tune control systems modeled in Simulink and analyze its time and frequency domain characteristics using Simulink Control Design. Linearize Simulink models and compute time and frequency responses. Graphically or automatically tune feedback loops modeled in Simulink.
Reference Applications
Use reference application examples for flight control, power electronics, robotics, and other applications to design and analyze controllers for systems modeled in MATLAB and Simulink.
Product Resources:
“Simulink enabled us to produce a stable control system in a short time. We modeled the entire system, including a state machine and cascaded PI controls. We refined this model to improve robustness and response times, then verified it with RCP, and generated embedded code.”
Control System Toolbox FAQs
Control System Toolbox is an add-on product for MATLAB that lets you systematically model, analyze, design, and tune linear control systems. It provides a comprehensive set of algorithms and interactive apps for working with dynamic systems represented as transfer functions, state-space models, zero-pole-gain models, and frequency-response data.
You can specify your system as a transfer function, state-space, zero-pole-gain, frequency-response data (FRD) model, or Linear Parameter Varying (LPV) and Linear Time Varying (LTV) models, in both continuous and discrete time.
You can visualize system behavior using apps and functions such as step response plots, Bode plots, root locus plots, Nichols charts, and Nyquist plots, and compute characteristics like rise time, overshoot, settling time, and gain and phase margins.
Yes, the toolbox automatically tunes both SISO and MIMO PID controllers using the PID Tuner app or command-line functions to balance performance and robustness.
You can use interactive techniques such as Bode loop shaping, root locus method, and the Control System Designer app for graphically tuning controllers.
Yes, the toolbox supports both SISO and MIMO control system design, including automatic tuning of decentralized control structures with multiple feedback loops.
The toolbox includes state-space control design methods such as LQR/LQG and pole-placement algorithms, plus state estimation using linear and nonlinear Kalman filters.
Yes, you can design gain-scheduled controllers for nonlinear or time-varying plants, specify requirements, automatically tune gain surface coefficients, and validate results across the entire operating range.
Yes. Control System Toolbox supports automatic code generation workflows. Controllers designed and validated in MATLAB can be deployed to embedded hardware using MATLAB Coder or Simulink Coder, supporting the path from design to production implementation.
Using Simulink Control Design, you can linearize Simulink models, analyze time and frequency domain characteristics, and graphically or automatically tune feedback loops modeled in Simulink.
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