An operating point of a dynamic system specifies the initial states and rootlevel input signals of the model at a particular time. For more information on operating points, see About Operating Points.
To find steadystate operating points you can use optimizationbased searching or simulation snapshots.
You can compute a steadystate operating point (or equilibrium operating point) using numerical optimization methods to meet your specifications. The resulting operating point consists of the equilibrium state values and corresponding model input levels. A successful operating point search finds an operating point very close to a true steadystate solution.
Use an optimizationbased search when you have knowledge about the operating point states and the corresponding model input and output signal levels. You can use this knowledge to specify initial guesses or constraints for the following variables at equilibrium:
Initial state values
States at equilibrium
Maximum or minimum bounds on state values, input levels, and output levels
Known (fixed) state values, input levels, or output levels
Your operating point search might not converge to a steadystate operating point when you overconstrain the optimization by specifying:
Initial guesses for steadystate operating point values that are far away from the desired steadystate operating point.
Incompatible input, output, or state constraints at equilibrium.
You can control the accuracy of your operating point search by configuring the optimization algorithm settings.
You can compute a steadystate operating point by simulating your model until it reaches a steadystate condition. To do so, specify initial conditions for the simulation that are near the desired steadystate operating point.
Use a simulation snapshot when the time it takes for the simulation to reach steady state is sufficiently short. The algorithm extracts operating point values once the simulation reaches steady state.
Simulationbased computations produce poor operating point results when you specify:
A simulation time that is insufficiently long to drive the model to steady state.
Initial conditions that do not cause the model to reach true equilibrium.
You can usually combine a simulation snapshot and an optimizationbased search to improve your operating point results. For example, simulate your model until it reaches the neighborhood of steady state and use the resulting simulation snapshot to define the initial conditions for an optimizationbased search.
Note
If your Simulink^{®} model has internal states, do not linearize the model at an operating point you compute from a simulation snapshot. Instead, try linearizing the model using a simulation snapshot or at an operating point from optimizationbased search. For more information, see Handle Blocks with Internal State Representation.
When computing a steadystate operating point, not all states are required to be at equilibrium. A pendulum is an example of a system where it is possible to find an operating point with all states at steady state. However, for other types of systems, there may not be an operating point where all states are at equilibrium, and the application does not require that all operating point states be at equilibrium.
For example, suppose that you build an automobile model for a cruise control application with these states:
Vehicle position and velocity
Fuel and air flow rates into the engine
If your goal is to study the automobile behavior at constant cruising velocity, you need an operating point with the velocity, air flow rate, and fuel flow rate at steady state. However, the position of the vehicle is not at steady state because the vehicle is moving at constant velocity. The lack of a steadystate position variable is fine for the cruise control application because the position does not have significant impact on the cruise control behavior. In this case, you do not need to overconstrain the optimization search for an operating point by requiring that all states be at equilibrium.
Similar situations also appear in aerospace systems when analyzing the dynamics of an aircraft under different maneuvers.
Simulink Control Design™ lets you search for operating points of your Simulink model both programmatically at the command line and interactively using one of two apps.
Search Tool  When to Use 

findop 

Steady State Manager 

Model Linearizer 

Simulink provides the trim
command for
steadystate operating point searches. However, findop
in
Simulink
Control Design provides several advantages over using
trim
when performing an optimizationbased
operating point search.
Simulink Control Design Operating Point Search  Simulink Operating Point Search  

User interface  Yes  No — Only trim is
available. 
Multiple optimization methods  Yes  No — Only one optimization method 
Constrain state, input, and output variables using upper and lower bounds  Yes  No 
Specify the output value of blocks that are not connected to root model outports  Yes  No 
Steadyoperating points for models with discrete states  Yes  No 
Model reference support  Yes  No 
Simscape™ Multibody™ integration  Yes  No 