This example shows how to optimize a design to meet custom objective using `sdo.optimize`

. You optimize the cylinder parameters to minimize the cylinder geometry and satisfy design requirements.

Open the Simulink model.

```
sys = 'sdoHydraulicCylinder';
open_system(sys);
```

The hydraulic cylinder model is based on the Simulink model `sldemo_hydcyl`

. The model includes:

`Pump`

and`Cylinder Assembly`

subsystems. For more information on the subsystems, see "Single Hydraulic Cylinder Simulation".

A step change applied to the cylinder control valve orifice area that causes the cylinder piston position to change.

You tune the cylinder cross-sectional area and piston spring constant to meet the following design requirements:

Ensure that the piston position has a step response rise time of less than 0.04 seconds and setting time of less than 0.05 seconds.

Limit the maximum cylinder pressures to 1.75e6 N/m.

Minimize the cylinder cross-sectional area.

Select the following model parameters as design variables for optimization:

Cylinder cross-sectional area

`Ac`

Piston spring constant

`K`

Ac = sdo.getParameterFromModel('sdoHydraulicCylinder','Ac'); K = sdo.getParameterFromModel('sdoHydraulicCylinder','K');

Limit the cylinder cross-sectional area to a circular area with radius between 1 and 2 centimeters.

Ac.Minimum = pi*1e-2^2; % m^2 Ac.Maximum = pi*2e-2^2; % m^2

Limit the piston spring constant to a range of 1e4 to 10e4 N/m.

K.Minimum = 1e4; % N/m K.Maximum = 10e4; % N/m

The design requirements require logged model signals. During optimization, the model is simulated using the current value of the design variables and the logged signal is used to evaluate the design requirements.

Log the following signals:

Cylinder pressures, available at the first output port of the

`Cylinder Assembly`

block

```
Pressures = Simulink.SimulationData.SignalLoggingInfo;
Pressures.BlockPath = 'sdoHydraulicCylinder/Cylinder Assembly';
Pressures.OutputPortIndex = 1;
```

Piston position, available at the second output port of the

`Cylinder Assembly`

block

```
PistonPosition = Simulink.SimulationData.SignalLoggingInfo;
PistonPosition.BlockPath = 'sdoHydraulicCylinder/Cylinder Assembly';
PistonPosition.OutputPortIndex = 2;
```

Create an object to store the logging information and use later to simulate the model

```
simulator = sdo.SimulationTest('sdoHydraulicCylinder');
simulator.LoggingInfo.Signals = [PistonPosition,Pressures];
```

Specify the piston position step response requirement of rise time of less than 0.04 seconds and settling time less than of 0.05 seconds.

PistonResponse = sdo.requirements.StepResponseEnvelope; set(PistonResponse, ... 'RiseTime', 0.04, ... 'FinalValue', 0.04, ... 'SettlingTime', 0.05, ... 'PercentSettling', 1);

Specify the maximum cylinder pressure requirement of less than 1.75e6 N/m.

MaxPressure = sdo.requirements.SignalBound; set(MaxPressure, ... 'BoundTimes', [0 0.1], ... 'BoundMagnitudes', [1.75e6 1.75e6], ... 'Type', '<=');

For convenience, collect the performance requirements into a single structure to use later.

requirements = struct(... 'PistonResponse', PistonResponse, ... 'MaxPressure', MaxPressure);

To optimize the cylinder cross-sectional area and piston spring constant, create a function to evaluate the cylinder design. This function is called at each optimization iteration.

Here, use an anonymous function with one argument that calls the `sdoHydraulicCylinder_design`

function.

evalDesign = @(p) sdoHydraulicCylinder_design(p,simulator,requirements);

The function:

Has one input argument that specifies the cylinder cross-sectional area and piston spring constant values.

Returns the optimization objective value and optimization constraint violation values.

The optimization solver minimizes the objective value and attempts to keep the optimization constraint violation values negative. Type `help sdoExampleCostFunction`

for more details on how to write the objective/constraint function.

The `sdoHydraulicCylinder_design`

function uses the `simulator`

and `requirements`

objects to evaluate the design. Type `edit sdoHydraulicCylinder_design`

to examine the function in more detail.

```
type sdoHydraulicCylinder_design
```

function design = sdoHydraulicCylinder_design(p,simulator,requirements) %SDOHYDRAULICCYLINDER_DESIGN % % The sdoHydraulicCylinder_design function is used to evaluate a cylinder % design. % % The |p| input argument is the vector of cylinder design parameters. % % The |simulator| input argument is a sdo.SimulinkTest object used to % simulate the |sdoHydraulicCylinder| model and log simulation signals % % The |requirements| input argument contains the design requirements used % to evaluate the cylinder design % % The |design| return argument contains information about the design % evaluation that can be used by the |sdo.optimize| function to optimize % the design. % % see also sdo.optimize, sdoExampleCostFunction % Copyright 2011 The MathWorks, Inc. %% Simulate the model % % Use the simulator input argument to simulate the model and log model % signals. % % First ensure that we simulate the model with the parameter values chosen % by the optimizer. % simulator.Parameters = p; % Simulate the model and log signals. % simulator = sim(simulator); % Get the simulation signal log, the simulation log name is defined by the % model |SignalLoggingName| property % logName = get_param('sdoHydraulicCylinder','SignalLoggingName'); simLog = get(simulator.LoggedData,logName); %% Evaluate the design requirements % % Use the requirements input argument to evaluate the design requirements % % Check the PistonPosition signal against the stepresponse requirement % PistonPosition = get(simLog,'PistonPosition'); cPiston = evalRequirement(requirements.PistonResponse,PistonPosition.Values); % Check the Pressure signals against the maximum requirement % Pressures = find(simLog,'Pressures'); cPressure = evalRequirement(requirements.MaxPressure,Pressures.Values); % Use the PistonResponse and MaxPressure requirements as non-linear % constraints for optimization. design.Cleq = [cPiston(:);cPressure(:)]; % Add design objective to minimize the Cylinder cross-sectional area Ac = p(1); %Since we called sdo.optimize(evalDesign,[Ac;K]) design.F = Ac.Value; end

Call the objective function with the initial cylinder cross-sectional area and initial piston spring constant.

initDesign = evalDesign([Ac;K]);

The function simulates the model and evaluates the design requirements. The scope shows that the maximum pressure requirement is satisfied but the piston position step response requirement is not satisfied.

`initDesign`

is a structure with the following fields:

`Cleq`

shows that some of the inequality constraints are positive indicating they are not satisfied by the initial design.

initDesign.Cleq

ans = -0.3839 -0.1861 -0.1836 -1.0000 0.3033 0.2909 0.1671 0.2326 -0.0480 -0.0480

`F`

shows the optimization objective value (in this case the cylinder cross-sectional area). The initial design cross-sectional area, as expected, has the same value as the initial cross-sectional area parameter`Ac`

.

initDesign.F

ans = 1.0000e-03

Pass the objective function, initial cross-sectional area and piston spring constant values to `sdo.optimize`

.

[pOpt,optInfo] = sdo.optimize(evalDesign,[Ac;K]);

Optimization started 26-Aug-2019 12:55:24 max First-order Iter F-count f(x) constraint Step-size optimality 0 5 0.001 0.3033 1 11 0.00057281 0.07293 0.48 85.4 2 17 0.000391755 0 0.128 28 3 22 0.000387625 0 0.00291 0.00459 4 27 0.000382932 0 0.00331 0.00187 5 32 0.000378204 0 0.00331 0.000219 Local minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance.

The optimization repeatedly evaluates the cylinder design by adjusting the cross-sectional area and piston spring constant to meet the design requirements. From the scope, see that the maximum pressure and piston response requirements are met.

The `sdo.optimize`

function returns:

`pOpt`

shows the optimized cross-sectional area and piston spring constant values.

pOpt

pOpt(1,1) = Name: 'Ac' Value: 3.7820e-04 Minimum: 3.1416e-04 Maximum: 0.0013 Free: 1 Scale: 0.0020 Info: [1x1 struct] pOpt(2,1) = Name: 'K' Value: 1.5809e+04 Minimum: 10000 Maximum: 100000 Free: 1 Scale: 65536 Info: [1x1 struct] 2x1 param.Continuous

`optInfo`

is a structure that contains optimization termination information such as number of optimization iterations and the optimized design.

optInfo

optInfo = struct with fields: Cleq: [10x1 double] F: 3.7820e-04 Gradients: [1x1 struct] exitflag: 1 iterations: 5 SolverOutput: [1x1 struct] Stats: [1x1 struct]

For example, the `Cleq`

field shows the optimized non-linear inequality constraints are all non-positive to with-in optimization tolerances, indicating that the maximum pressure and piston response requirements are satisfied.

optInfo.Cleq

ans = -0.0972 -0.0131 -0.0131 -1.0000 -0.2064 -0.0047 -0.0069 -0.0004 -0.0476 -0.0476

The `F`

field contains the optimized cross-sectional area. The optimized cross-sectional area value is nearly 50% less that the initial value.

optInfo.F

ans = 3.7820e-04

By default, the model variables `Ac`

and `K`

are not updated at the end of optimization. Use the `setValueInModel`

command to update the model variable values.

```
sdo.setValueInModel('sdoHydraulicCylinder',pOpt)
```

To learn how to optimize the cylinder design using the Response Optimization tool, see "Design Optimization to Meet Custom Objective (GUI)".

% Close the model bdclose('sdoHydraulicCylinder')