Main Content

simulink.schedule.OrderedSchedule Class

Namespace: simulink.schedule

Creates an OrderedSchedule object containing priority order of the partitions of a model

Since R2020a

expand all in page

Description

The simulink.schedule.OrderedSchedule object is a representation of the execution order of the partitions of the specified model. Access this object as a model parameter with get_param. You can use the OrderedSchedule object to modify the schedule of the partitions of the model through the command line.

You can use set_param to apply the schedule to the model.

Creation

get_param(mdl,'Schedule') creates an OrderedSchedule object for the specified model, mdl.

Properties

expand all

Priority order of the partitions, specified as a table where:

  • The row names are names of the partitions.

  • The first column is the index of the partition. Modify the index to change the order of the partitions.

  • The second column shows the type of the partitions.

  • The third column shows the trigger of the partition. This column lists either the sample time of the partitions, or the hit times at which the partitions execute.

RateSections is an array of objects containing a portion of the order table with a single rate. Use RateSections to easily modify the order of the execution of partitions within valid groups.

Purpose of an individual schedule object, specified as a string.

Schedule Editor events that may be triggers for aperiodic partitions. Schedule Editor events are sent by Stateflow® chart and by Input Events in Simulink®, specified as an array of simulink.schedule.Event objects.

Examples

collapse all

Open Script

This example uses the Schedule Editor API to perform operations on the schedule. Then it uses a function to generate random schedules and analyze them in Simulation Data Inspector

Open the Model and Get the Schedule Object

Open a model of a Throttle Position Control system and use get_param to obtain the simulink.schedule.OrderedSchedule object. This object contains the current schedule.

model = 'ScheduleEditorAPIWithSubsystemPartitions';
open_system(model);
schedule = get_param(model,'Schedule')

schedule = 

  OrderedSchedule with properties:

           Order: [7x3 table]
    RateSections: [3x1 simulink.schedule.RateSection]
          Events: [0x1 simulink.schedule.Event]
     Description: ''

Examine the Schedule Object

The schedule object has an Order property that contains the execution order of the partitions in the model. The Order property displays a table that contains partition names, their index, type, and their trigger.

schedule.Order

ans =

  7x3 table

                          Index      Type      Trigger
                          _____    ________    _______

    Cont                    1      Periodic    "0"    
    TPSSecondaryRun5ms      2      Periodic    "0.005"
    MonitorRun5ms           3      Periodic    "0.005"
    ControllerRun5ms        4      Periodic    "0.005"
    ActuatorRun5ms          5      Periodic    "0.005"
    APPSnsrRun              6      Periodic    "0.01" 
    TPSPrimaryRun10ms       7      Periodic    "0.01"

Use the index variable in the Order table to change the execution order of the model

schedule.Order.Index('ActuatorRun5ms') = 2;
schedule.Order

ans =

  7x3 table

                          Index      Type      Trigger
                          _____    ________    _______

    Cont                    1      Periodic    "0"    
    ActuatorRun5ms          2      Periodic    "0.005"
    TPSSecondaryRun5ms      3      Periodic    "0.005"
    MonitorRun5ms           4      Periodic    "0.005"
    ControllerRun5ms        5      Periodic    "0.005"
    APPSnsrRun              6      Periodic    "0.01" 
    TPSPrimaryRun10ms       7      Periodic    "0.01"

Any moves within the Order property that are made to modify the schedule should result in valid schedule. To perform the schedule modifications and valid moves easier, each partition is grouped with partitions of the same rate in the RateSections property. Each element of the RateSection property contains an order table with partitions of the same rate.

schedule.RateSections(2)
schedule.RateSections(2).Order

ans = 

  RateSection with properties:

     Rate: "0.005"
    Order: [4x3 table]


ans =

  4x3 table

                          Index      Type      Trigger
                          _____    ________    _______

    ActuatorRun5ms          2      Periodic    "0.005"
    TPSSecondaryRun5ms      3      Periodic    "0.005"
    MonitorRun5ms           4      Periodic    "0.005"
    ControllerRun5ms        5      Periodic    "0.005"

Use the index variable to move the partitions within RateSections.

schedule.RateSections(2).Order.Index('ActuatorRun5ms') = 5;
schedule.Order

ans =

  7x3 table

                          Index      Type      Trigger
                          _____    ________    _______

    Cont                    1      Periodic    "0"    
    TPSSecondaryRun5ms      2      Periodic    "0.005"
    MonitorRun5ms           3      Periodic    "0.005"
    ControllerRun5ms        4      Periodic    "0.005"
    ActuatorRun5ms          5      Periodic    "0.005"
    APPSnsrRun              6      Periodic    "0.01" 
    TPSPrimaryRun10ms       7      Periodic    "0.01"

Create a Function to Generate Random Schedules

In this section, we create three different functions: randomSchedule, generateSimulationInputs and simulateRandomSchedules

randomSchedule function is used to create random schedules by using random permutations of index modifications in the schedule object. Using the Order and the RateSections properties of the schedule object, partitions in the schedules are moved around in different, random combinations. With these randomly created schedules, models are simulated and compared to study the effect of different schedules on simulation. In the function randomSchedule, the input is the model name. Then use get_param to obtain the simulink.schedule.OrderedSchedule object of the model. The schedule object and its properties are used to modify and randomize the schedules. Create a variable firstExecutionOrder for the first rate section of the model. The rateSections(1).ExecutionOrder = [firstExecutionOrder(1,:); reSchedule(firstExecutionOrder(2:end,:))] line of code calls the function reSchedule which creates random permutations of the indexes.

type randomSchedule

function schedule = randomSchedule(model)
    % schedule = randomSchedule(model) Produces a
    % simulink.schedule.OrderedSchedule that has a randomized permutation
    % of the model's original execution order schedule
    
    arguments
        model char = bdroot
    end
    
    schedule = get_param(model, 'Schedule');
    
    rateSections = schedule.RateSections;
    firstOrder = rateSections(1).Order;
    
    % This assumes that the slowest discrete rate is at index 1. This may
    % not be the case for all models (ex. JMAAB-B).
    rateSections(1).Order = [firstOrder(1,:); reSchedule(firstOrder(2:end,:))];    
    
    for i=2:length(rateSections)
        rateSections(i).Order = reSchedule(rateSections(i).Order);
    end
    
    schedule.RateSections = rateSections;
end

function out = reSchedule(in)
    numPartitions = height(in);
    in.Index = in.Index(randperm(numPartitions));
    out = in;
end

To analyze the effects of different schedules on the model, simulate the model with the different schedules. In this function, create an array of Simulink.SimulationInput objects. Through this array of Simulink.SimulationInput objects, you can apply the schedules to the model with the setModelParameters method of the Simulink.SimulationInput object.

type generateSimulationInputs

function in = generateSimulationInputs(model, numSimulations)
    % in = generateSimulationInputs(model, numSimulations) Generates
    % numSimulations Simulink.SimulationInput objects each containing a
    % different, randomized execution order schedule
    arguments
        model char = bdroot
        numSimulations double = 10
    end
    
    in(numSimulations) = Simulink.SimulationInput();
    in = in.setModelName(model);
    for idx = 1:numSimulations
        in(idx) = in(idx).setModelParameter('Schedule', randomSchedule(model));
    end
end

In the last function, use the array of Simulink.SimulationInput objects to run multiple simulations. Once the simulations are complete, you can plot the output of all the simulations in Simulation Data Inspector.

type simulateRandomSchedules

function out = simulateRandomSchedules(model, numSimulations)
    % out = simulateRandomSchedules(model, numSimulations) Simulates a 
    % model numSimulations number of times.  Each simulation has a
    % randomized execution order schedule.
    arguments
        model char = bdroot
        numSimulations double = 10
    end
        
    in = generateSimulationInputs(model, numSimulations);
    out = sim(in);
    plot(out);
end

Execute the Functions

Now run the above functions for the ScheduleEditorAPIWithSubsystemPartitions model. First, use the randomSchedule function to create randomly generated schedules, then, use the generateSimulationInputs function to generate an array of Simulink.SimulationInput objects, and use the simulateRandomSchedule function to simulate the model with different schedules and plot their results for comparison. Let's run simulations with 15 randomly generated schedules.

simulateRandomSchedules(model,15)

[05-Sep-2024 18:56:39] Running simulations...
[05-Sep-2024 18:56:49] Completed 1 of 15 simulation runs
[05-Sep-2024 18:56:51] Completed 2 of 15 simulation runs
[05-Sep-2024 18:56:53] Completed 3 of 15 simulation runs
[05-Sep-2024 18:56:55] Completed 4 of 15 simulation runs
[05-Sep-2024 18:56:56] Completed 5 of 15 simulation runs
[05-Sep-2024 18:56:58] Completed 6 of 15 simulation runs
[05-Sep-2024 18:56:59] Completed 7 of 15 simulation runs
[05-Sep-2024 18:57:00] Completed 8 of 15 simulation runs
[05-Sep-2024 18:57:02] Completed 9 of 15 simulation runs
[05-Sep-2024 18:57:03] Completed 10 of 15 simulation runs
[05-Sep-2024 18:57:05] Completed 11 of 15 simulation runs
[05-Sep-2024 18:57:06] Completed 12 of 15 simulation runs
[05-Sep-2024 18:57:07] Completed 13 of 15 simulation runs
[05-Sep-2024 18:57:08] Completed 14 of 15 simulation runs
[05-Sep-2024 18:57:10] Completed 15 of 15 simulation runs

ans = 

1x15 Simulink.SimulationOutput array

Version History

Introduced in R2020a

See Also

| | |

Topics