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This example shows how to use the `systune`

command to tune Field-Oriented Control (FOC) for a permanent magnet synchronous machine (PMSM) based on frequency response estimation (FRE) result.

In this example, field-oriented control (FOC) for a permanent magnet synchronous machine (PMSM) is modeled in Simulink® using Simscape™ Electrical™ Power Systems components.

mdl = 'scdfocmotorSystune'; open_system(mdl) SignalBuilderPath = [mdl,'/System_Inputs/Signal_Builder_Experiments'];

Field-oriented control (FOC) controls 3-phase stator currents as a vector. FOC is based on projections, which transform a 3-phase time- and speed-dependent system into a two coordinate time-invariant system. These transformations are the Clarke Transformation, Park Transformation and their respective inverse transforms. These transformations are implemented as blocks within the Controller_Algorithm subsystem.

The advantages of using FOC to control AC motors include:

Torque and flux controlled directly and separately

Accurate transient and steady-state management

Similar performance compared to DC motors

The Controller_Algorithm subsystem contains all three PI controllers. The outer-loop PI controller regulates the speed of the motor. The two inner-loop PI controllers control the d-axis and q-axis currents separately. The command from the outer loop PI controller directly feeds to the q-axis to control torque. The command for the d-axis is zero for PMSM because the rotor flux is fixed with a permanent magnet for this type of AC motor.

Before tuning controllers, examine the speed responses with the original controllers, and save the simulation results to a MAT-file, `SystunedSpeed.mat`

. The existing speed PI controller has gains of P = 0.08655 and I = 0.1997. The current PI controllers both have gains of P = 1 and I = 200.

scdfocmotorSystuneOriginalResponse

To collect frequency response data, find an operating point at a speed of `150`

rad/sec, specify linear analysis points, define input signals, and estimate the frequency response.

Disconnect the original controllers, and simulate the open-loop system model with VD and VQ commands. To reach the operating point, specify initial voltages of -0.1 V for VD and 3.465 V for VQ using the `ctrlIniValues`

structure. Constant voltage command blocks are connected by setting switch signals in the `switchIniValue`

structure.

switchIniValue.openLoopD = 1; switchIniValue.openLoopQ = 1; ctrlIniValues.voltageD = -0.1; ctrlIniValues.voltageQ = 3.465;

Capture a simulation snapshot at `3`

sec as the operating point for frequency response estimation.

```
signalbuilder(SignalBuilderPath, 'activegroup', 1);
op = findop(mdl,3);
```

Use the simulation snapshot operating point as the initial condition of the model. Change the model initial values in the `ctrlIniValues`

structure to be at this steady state. For the d-axis current controller, the current ID is `0`

A. For the q-axis current controller, the current IQ is `0.1`

A. For the outer-loop speed controller, the reference current is `0.122`

A and the speed is at `150`

rad/s. For the PMSM plant, set the rotor velocity in the `pmsm`

structure to `150`

rad/s.

set_param(mdl,'LoadInitialState','on'); set_param(mdl,'InitialState','getstatestruct(op)'); ctrlIniValues.currentDIC = 0; ctrlIniValues.currentQIC = 0.1; ctrlIniValues.speedIC = 150; ctrlIniValues.speedCurrent = 0.122; pmsm.RotorVelocityInit = 150;

Add linear analysis points to the model for frequency response estimation. Add open-loop input points to VD and VQ. Add open-loop output points to ID, IQ, and speed. In addition, add a loop break analysis point to the speed measurement.

io = getlinio(mdl);

Define input sinestream signals from `10`

to `10,000`

rad/sec with a fixed sample time of `4e-6`

sec. The sinestream signal magnitude is `0.25`

V. This magnitude ensures that the plant is properly excited within the saturation limit. If the excitation amplitude is either too large or too small, it will produce inaccurate frequency response estimation results.

in = frest.createFixedTsSinestream(4e-6,{10,1e4}); in.Amplitude = 0.25;

Estimate the frequency response at the specified steady state operating point `op`

, using the linear analysis points in `io`

and the input signals in `in`

. After finishing the frequency response estimation, modify the input and output channel names in the resulting model, and plot the frequency response.

estsys = frestimate(mdl,op,io,in); estsys.InputName = {'Vd','Vq'}; estsys.OutputName = {'Id','Iq','speed'}; bode(estsys,'.')

`systune`

Obtain a state-space linear system model from the frequency response estimation result. Using an option set for the `ssest`

function, set the numerical search method used for this iterative parameter estimation is the Levenberg-Marquardt least-squares search. Estimate a state-space model with four states and a period of `4e-6`

seconds.

optssest = ssestOptions('SearchMethod','lm'); optssest.Regularization.Lambda = 0.1; sys_singletune = ssest(estsys,4,'Ts',4e-6,optssest);

In order to tune all three PI controllers in the PMSM FOC model, we need to construct a control system, as shown in the following block diagram.

Define three tunable discrete-time PID blocks and their I/Os for d-axis current control, q-axis current control, and speed control.

Cd = tunablePID('Cd','pi',4e-6); Cd.u = 'Id_e'; Cd.y = 'Vd'; Cq = tunablePID('Cq','pi',4e-6); Cq.u = 'Iq_e'; Cq.y = 'Vq'; Cspeed = tunablePID('Cspeed','pi',4e-6); Cspeed.u = 'speed_e'; Cspeed.y = 'Iq_ref';

Create three summing junctions for the inner and out feedback loops.

sum_speed = sumblk('speed_e = speed_ref - speed'); sum_id = sumblk('Id_e = Id_ref - Id'); sum_iq = sumblk('Iq_e = Iq_ref - Iq');

Define inputs, outputs, and analysis points for controller tuning.

input = {'Id_ref','speed_ref'}; output = {'Id','Iq','speed'}; APs = {'Iq_ref','Vd','Vq','Id','Iq','speed'};

Finally, assemble the complete control system, `ST0`

, using these components.

ST0 = connect(sys_singletune,Cd,Cq,Cspeed,sum_speed,sum_id,sum_iq,input,output,APs);

Define tuning goals, including tracking and loop shape goals to ensure command tracking, as well as gain goals to prevent saturations. For the speed controller, set the tracking bandwidth to `150`

rad/s. This bandwidth is used in both the tracking and loop shape goals. In addition, set the DC error to `0.001`

to reflect a maximum steady-state error of `0.1`

%. Set the peak error to `10`

. For the d-axis current controller, set the tracking bandwidth to `2500`

rad/s, which is much faster than the outer-loop speed controller. To prevent saturating controllers, specify goals to constrain the gains for all three controllers.

TR1 = TuningGoal.Tracking('speed_ref','speed',2/150,0.001,10); TR2 = TuningGoal.Tracking('Id_ref','Id',2/2500); LS1 = TuningGoal.LoopShape('Id',2500); LS2 = TuningGoal.LoopShape('speed',150); MG1 = TuningGoal.Gain('speed_ref','Iq_ref',2); MG2 = TuningGoal.Gain('speed_ref','Vq',50); MG3 = TuningGoal.Gain('Id_ref','Vd',20);

Tune all three PI controllers using `systune`

with all tuning goals based on the constructed model `ST0`

. To increase the likelihood of finding parameter values that meet all design requirements, set options for `systune`

to run five additional optimizations starting from five randomly generated parameter values.

```
opt = systuneOptions('RandomStart',5);
rng(2);
[ST1,fSoft] = systune(ST0,[TR1,TR2,LS1,LS2,MG1,MG2,MG3],opt);
```

Final: Soft = 1.03, Hard = -Inf, Iterations = 104 Some closed-loop poles are marginally stable (decay rate near 1e-07) Final: Soft = 1.01, Hard = -Inf, Iterations = 65 Some closed-loop poles are marginally stable (decay rate near 1e-07) Final: Soft = 1.01, Hard = -Inf, Iterations = 62 Final: Soft = 1.01, Hard = -Inf, Iterations = 68 Some closed-loop poles are marginally stable (decay rate near 1e-07) Final: Soft = 1.01, Hard = -Inf, Iterations = 63 Final: Soft = 1.01, Hard = -Inf, Iterations = 50

After finding a solution using `systune`

, show how tuning goals are met in the tuned model `ST1`

. Show the tracking, loop shape, and gain tuning goals separately. Dashed lines in the following figures represent tuning goals and solid lines are the result of the tuned controllers.

figure viewGoal([TR1,TR2],ST1); figure viewGoal([LS1,LS2],ST1); figure viewGoal([MG1,MG2,MG3],ST1);

After verifying tuning goals, extract controller parameters from the tuned model `ST1`

. Use tuned PI controller parameters to update the workspace parameters for the PI controller blocks.

Cd = getBlockValue(ST1,'Cd'); Cq = getBlockValue(ST1,'Cq'); Cspeed = getBlockValue(ST1,'Cspeed'); paramCurrentControlPD = Cd.Kp; paramCurrentControlID = Cd.Ki; paramCurrentControlPQ = Cq.Kp; paramCurrentControlIQ = Cq.Ki; paramVelocityControlTuneP = Cspeed.Kp; paramVelocityControlTuneI = Cspeed.Ki;

After tuning all three controllers together using `systune`

, the controller gains are:

The speed PI controller has gains of P = 0.3609 and I = 0.1434.

The d-axis current PI controller has gains of P = 2.7877 and I = 2676.7.

The q-axis current PI controller has gains of P = 1.3371 and I = 881.3072.

Examine the performances using the tuned controller gains. First, initialize the model to its zero initial conditions using `ctrlIniValues`

. Connect the PID controller blocks by setting switch signals in the `switchIniValue`

and set proper initial conditions for the PMSM plant model.

switchIniValue.openLoopQ = 0; switchIniValue.openLoopD = 0; ctrlIniValues.currentDIC = 0; ctrlIniValues.voltageD = 0; ctrlIniValues.currentQIC = 0; ctrlIniValues.voltageQ = 0; ctrlIniValues.speedIC = 0; ctrlIniValues.speedCurrent = 0; pmsm.RotorVelocityInit = 0; set_param(mdl,'LoadInitialState','off');

Configure the model to use a one-sided speed command signal and simulate the model. Show the speed response of the model to the one-sided speed command that rises from `0`

rad/s to `150`

rad/s at `0.05`

sec, and then to `200`

rad/s at `0.8`

sec. Save the simulation result to `logsout_tuned_oneside`

in the MAT-file, `SystunedSpeed.mat`

.

signalbuilder(SignalBuilderPath, 'activegroup', 2); sim(mdl); logsout_tuned_oneside = logsout; save('SystunedSpeed','logsout_tuned_oneside','-append')

Configure the model to use a two-sided speed command signal and simulate the model. Show the speed response of the model to the two-sided speed command that rises from `0`

rad/s to `150`

rad/s at `0.05`

sec, reverses direction at `0.5`

sec and then back to `0`

rad/s at `0.8`

sec. Save the simulation result to `logsout_tuned_twoside`

in the MAT-file. `SystunedSpeed.mat`

.

signalbuilder(SignalBuilderPath, 'activegroup', 3); sim(mdl); logsout_tuned_twoside = logsout; save('SystunedSpeed','logsout_tuned_twoside','-append')

Compare the motor speed responses between the existing controller gains and the tuned result. The speed responses are shown side-by-side over the one-second simulation. The speed response follows more closely to the step command. The steady-state error also decreases after the PI controllers are tuned with `systune`

.

scdfocmotorSystunePlotSpeed

After tuning the controllers, the motor response improves with faster transient response and smaller steady-state error under both types of speed commands.

bdclose(mdl)