Control Design in Simulink
Simulink® Design Optimization™ software provides several ways to specify design requirements. You can use the Check blocks to specify typical control design requirements such as reference tracking and satisfying signal bounds. You can then optimize the controller parameters in your model to meet these design requirements using the Response Optimizer app or at the command line.
|Response Optimizer||Optimize model response to satisfy design requirements, test model robustness|
|Check Against Reference||Check that model signal tracks reference signal during simulation|
|Check Custom Bounds||Check that model signal satisfies bounds during simulation|
|Check Step Response Characteristics||Check that model signal satisfies step response bounds during simulation|
Configure Cost Function
Create Simulation Scenario
|Simulation scenario description|
Specify Time-Domain Requirements
|Piecewise-linear amplitude bound|
|Reference signal to track|
|Step response bound on signal|
|Impose elliptic bound on phase plane trajectory of two signals|
|Impose region bound on phase plane trajectory of two signals|
Specify Variable Requirements
|Impose function matching constraint on variable|
|Impose monotonic constraint on variable|
|Impose relational constraint on pair of variables|
|Impose bounds on gradient magnitude of variable|
Specify Frequency-Domain Requirements
|Bode magnitude bound|
|Closed loop peak gain bound|
|Gain and phase margin bounds|
|Nichols response bound|
|Damping ratio bound|
|Natural frequency bound|
|Settling time bound|
|Singular value bound|
Configure Cost Function Optimization
|Solve design optimization problem|
|Optimization option set for |
|Get design variables for optimization|
|Set design variable value in model|
|Get design variable value from model|
|List of model file and path dependencies|
|Update model containing Signal Constraint block|
|Get bounds specified in Check block|
|Enable or disable all check blocks in model|
- How the Optimization Algorithm Formulates Minimization Problems
When you optimize parameters of a Simulink model to meet design requirements, Simulink Design Optimization software automatically converts the requirements into a constrained optimization problem and then solves the problem using optimization techniques.
- Design Optimization to Meet Step Response Requirements (GUI)
Optimize controller parameters to meet step response requirements using Response Optimizer.
- Design Optimization to Track Reference Signal (GUI)
Optimize parameters without adding Signal Constraint blocks to the model.
- Design Optimization to Meet Frequency-Domain Requirements (GUI)
This example shows how to tune model parameters to meet frequency-domain requirements using the Response Optimizer app.
- Design Optimization to Meet Frequency-Domain Requirements (Code)
This example shows how to tune model parameters to meet frequency-domain requirements, using the
- Design Optimization Using Frequency-Domain Check Blocks (GUI)
Optimize model parameters to meet frequency-domain design requirements using the Response Optimizer.
- Design Optimization to Meet Time-Domain and Frequency-Domain Requirements (GUI)
This example shows how to tune a controller to satisfy time-domain and frequency-domain design requirements using the Response Optimizer.
- Design Optimization to Meet Step Response Requirements (Code)
Optimize controller parameters at the command line.
- Write a Cost Function
Write a cost function for parameter estimation, response optimization, or sensitivity analysis. The cost function evaluates your design requirements using design variable values.
- Supported Design Requirements
Time-domain and frequency-domain requirements.
- Specify Time-Domain Design Requirements in the App
Specify time-domain requirements such as lower and upper amplitude bounds, step response bounds, reference signals, elliptical bounds, and custom bounds.
- Specify Variable Requirements in the App
Specify monotonic, smoothness, and relational constraints on variables in your model.
- Specify Frequency-Domain Design Requirements in the App
Specify frequency-domain requirements, such as gain and phase margin bounds, closed-loop peak response bounds, step-response bounds, and custom bounds.
Speed Up Optimization
- Skip Model Simulation Based on Parameter Constraint Violation (GUI)
This example shows how to optimize a design and specify parameter-only constraints that prevent the model from being evaluated in an invalid solution space.
- Speed Up Response Optimization Using Parallel Computing
Scenarios when you can speed up optimization using parallel computing, and how the speedup happens.
- Use Parallel Computing for Response Optimization
Use parallel computing for response optimization in the app, or at the command line.
- Use Fast Restart Mode During Response Optimization
This topic shows how to speed up response optimization using Simulink fast restart.
- Use Accelerator Mode During Simulations
Simulink Design Optimization software supports
Response Optimizer Tasks
- Specify Design Variables for Optimization
Specify continuous and discrete variables to tune in the Response Optimizer app, including initial values and allowed ranges or values.
- Specify Signals to Log
Specify signals to log in the Response Optimizer.
- Create Linearization I/O Sets
Create linearization input/output sets in the Response Optimizer or Sensitivity Analyzer.
- Compare Requirements and Design Variables Using Spider Plot
This example shows how to use a spider plot to compare requirement evaluations before and after optimizing the response.
- Generate MATLAB Code for Design Optimization Problems (GUI)
This example shows how to automatically generate a MATLAB® function to solve a Design Optimization problem.
Optimization Does Not Make Progress
What to do if the optimization stalls or no changes are seen in parameters values.
What to do if the optimization does not satisfy design requirements or takes a long time to converge near a solution, or if the system response becomes unstable.
Optimization Speed and Parallel Computing
What to do if no speedup is seen with parallel computing, if the results are different, or if the optimization stalls.
What to do if optimization gives undesirable parameter values or violates bounds on values.
Reverting to Initial Parameter Values
How to quit optimizing and revert to original values.