Tuning, Analysis, and Validation
After your model is set up for tuning, the
adjusts the tunable coefficients to meet your design requirements.
To validate the result, examine system responses and evaluate how
closely tuning goals are met. For a control system modeled in Simulink®,
write the tuned parameter values to the model for validation against
the full nonlinear system. For more information, see Validate Tuned Control System.
Tune and Analyze Simulink Models
|Tune control system parameters in Simulink using
|Set options for
|Transfer function for specified I/O set using
|Open-loop transfer function at specified point using
|Sensitivity function at specified point using
|Complementary sensitivity function at specified point
|Update block values in Simulink model
Tune and Analyze MATLAB Models
|Tune fixed-structure control systems modeled in MATLAB
|Set options for
|Closed-loop transfer function from generalized model of control system
|Open-loop transfer function of control system represented by
|Sensitivity function from generalized model of control system
|Complementary sensitivity function from generalized model of control system
- Tune Control System at the Command Line
After building a tunable control system model and specifying tuning goals, use
systuneto tune the parameters of your system.
- Tuning Multiloop Control Systems
Jointly tune the inner and outer loops of a cascade architecture with
- Speed Up Tuning with Parallel Computing Toolbox Software
If you have the Parallel Computing Toolbox™ software installed, you can speed up the tuning of fixed-structure control systems.
Analysis and Validation
- Interpret Numeric Tuning Results
Tuning reports from
systuneand Control System Tuner give you an overview of how well the tuned control system meets your tuning goals.
- Visualize Tuning Goals
Tuning-goal plots let you visualize your design requirements against tuned system responses, to see where and by how much tuning goals are satisfied or violated.
- Validate Tuned Control System
When you tune a control system, validate the results by examining system responses with the tuned parameters.
- Extract Responses from Tuned MATLAB Model at the Command Line
Analyze responses of a tuned control system by using
getIOTransferand related functions to compute responses between various points in the model.
- Validating Results
This example shows how to interpret and validate tuning results from
Motion Control Applications
Industrial Automation Applications
- PID Tuning for Setpoint Tracking vs. Disturbance Rejection
Explore trade-offs between setpoint tracking and disturbance rejection when tuning PID controllers with
- Digital Control of Power Stage Voltage
Tune a high-performance digital controller with bandwidth close to the sampling frequency.
- MIMO Control of Diesel Engine
systuneto design and tune a MIMO controller for a Diesel engine. The controller is tuned in discrete time for a single operating condition.
- Active Vibration Control in Three-Story Building
systuneto control seismic vibrations in a three-story building.
- Vibration Control in Flexible Beam
systuneto tune a passivity-based controller for reducing vibrations in a flexible beam.
- Passive Control with Communication Delays
Use a passive control system to mitigate communication delays.
- Multiloop Control of a Helicopter
systuneto tune a multi-loop controller for a rotorcraft.
- Fixed-Structure Autopilot for a Passenger Jet
systuneto tune the standard configuration of a longitudinal autopilot.
- Fault-Tolerant Control of a Passenger Jet
Achieve reliable control of a passenger jet by tuning a controller for multiple operating modes of the plant.