# Create Black-Box Models of a Robotic Arm System Using Neural Networks

This example shows how to use neural networks as the nonlinear function when you model nonlinear ARX and Hammerstein-Wiener models. It compares the nonlinear systems estimated using different types of neural networks, activation functions, and number of layers.

### Example System: Industrial Robot Arm

Consider a robot arm described by a nonlinear three-mass flexible model. The input to the robot is the applied torque $$u(t)=\tau (t)$$ generated by the electrical motor. The resulting angular velocity of the motor $$y(t)=\frac{d}{dt}{q}_{m}(t)$$ is the measured output.

You can derive the equations of motion for this system. For example, a nonlinear state-space description of this system uses five states. For more information on this system, see the example Modeling an Industrial Robot Arm.

The data sets used in this example are saved in `robotarmdata.mat`

.

#### Prepare Data

Load the data sets, prepare both the estimation and validation data, and downsample the data 10 times for this example.

load robotarmdata.mat eData = iddata(ye,ue,5e-4,InputName="Torque", ... OutputName="Angular Velocity",Tstart=0); eData = idresamp(eData,[1 10]); eData.Name = "estimation data"

eData = Time domain data set with 1984 samples. Sample time: 0.005 seconds Name: estimation data Outputs Unit (if specified) Angular Velocity Inputs Unit (if specified) Torque Data Properties

vData = iddata(yv3,uv3,5e-4,InputName="Torque", ... OutputName="Angular Velocity",Tstart=0); vData = idresamp(vData,[1 10]); vData.Name = "validation data"; idplot(eData,vData) legend show

#### Specify Linear Regressor Object

Specify the input and output names of the regressors.

output_name = "Angular Velocity"; input_name = "Torque"; names = [output_name,input_name];

To choose the correct lags for the regressors, perform a linear estimation based order search using the `arxstruc`

and `selstruc`

commands. Search through all orders in the range `1:10`

for the output, `1:10`

for the input, and `0:2`

for the minimum input delay.

na_range = 1:10; nb_range = 1:10; nk_range = 0:2; NN = struc(na_range,nb_range,nk_range); V = arxstruc(eData,vData,NN); selstruc(V)

`ans = `*1×3*
3 5 0

The dialog box suggests that the orders leading to the best fit with the validation data are `na=3`

, `nb=6`

, and `nk=1`

, where `na`

is the output order, `nb`

is the input order, and `nk`

is the input-output delay. But a slightly smaller order using `na=3`

, `nb=5`

, and `nk=0`

seems to work almost as well. Select the smaller order values. This choice, where `nk=0`

, implies that the model has feedthrough.

Specify a linear regressor object with output and input regressor sets containing three and five regressors with lags ranging from 1 to 3 and 0 to 4, respectively.

output_lag = 1:3; input_lag = 0:4; lags = {output_lag,input_lag};

Create the linear regressor object.

lreg = linearRegressor(names,lags);

Get the regressors used by the model.

getreg(lreg)

`ans = `*8×1 string*
"Angular Velocity(t-1)"
"Angular Velocity(t-2)"
"Angular Velocity(t-3)"
"Torque(t)"
"Torque(t-1)"
"Torque(t-2)"
"Torque(t-3)"
"Torque(t-4)"

This linear regressor object is equivalent to the ARX model order matrix `[na nb nk]=[3 5 0]`

.

### Nonlinear ARX Model Identification

Identify nonlinear ARX models by using different configurations of neural networks for mapping the regressors to the output.

#### Use Regression Neural Network

First, try a regression neural network. Create a neural network object with one hidden layer of size 5 and one hidden layer of size 3 by using `idNeuralNetwork`

. Set `NetworkType`

to "`RegressionNeuralNetwork`

(Statistics and Machine Learning Toolbox)". Specify the activation function of both the layers as `relu`

.

net1 = idNeuralNetwork([5 3],"relu",NetworkType="RegressionNeuralNetwork");

Prepare training options by setting `Focus`

to `"simulation"`

using `nlarxOptions`

. This setting is to help the model perform well for long horizon predictions and amounts to training the network in a recurrent (closed-loop) configuration. Estimate the nonlinear ARX model that uses `net1`

as the output function using `nlarx`

. Set the random number generator seed for reproducibility.

rng default opt = nlarxOptions(Focus="simulation",Display="on"); nlarx1 = nlarx(eData,lreg,net1,opt)

nlarx1 = Nonlinear ARX model with 1 output and 1 input Inputs: Torque Outputs: Angular Velocity Regressors: Linear regressors in variables Angular Velocity, Torque List of all regressors Output function: Regression neural network Sample time: 0.005 seconds Status: Estimated using NLARX on time domain data "estimation data". Fit to estimation data: 75.05% (simulation focus) FPE: 1.169, MSE: 27 Model Properties

#### Use Deep Learning Network

Next, create a deep learning network object that contains three hidden layers using `idNeuralNetwork`

. Specify that the first hidden layer uses 10 units of the `tanh`

activation function, the second hidden layer uses 5 units of the `sigmoid`

activation function, and the last layer uses 2 units of the `gelu`

activation function.

LayerSizes = [10 5 2]; net2 = idNeuralNetwork(LayerSizes,["tanh","sigmoid","gelu"],NetworkType="dlnetwork");

To speed up the training, split the data into segments of 500 samples each with no overlap.

eDataSplit = utSegmentData(eData,500,500);

Use Levenberg-Marquardt as the search method with a maximum of five iterations. Estimate the nonlinear ARX model with `net2`

as the output function using `nlarx`

.

```
opt.SearchMethod = "lm";
opt.SearchOptions.MaxIterations = 5;
nlarx2 = nlarx(eDataSplit,lreg,net2,opt)
```

nlarx2 = Nonlinear ARX model with 1 output and 1 input Inputs: Torque Outputs: Angular Velocity Regressors: Linear regressors in variables Angular Velocity, Torque List of all regressors Output function: Deep learning network Sample time: 0.005 seconds Status: Estimated using NLARX on time domain data "estimation data". Fit to estimation data: [67.8 19.65 72.36]% (simulation focus) FPE: 1.264, MSE: [36.23 23.83 26.15] Model Properties

Compare the model fit to the original, unsegmented estimation data.

compare(eData,nlarx2)

#### Use Shallow Network

You can also use shallow networks to represent the neural network function map. Create a feedforward (shallow) network that uses three hidden layers of size 4, 6, and 1 using `feedforwardnet`

(Deep Learning Toolbox). The network uses the `tanh`

activation function.

snet = feedforwardnet([4 6 1]);

Incorporate the shallow network into the `idNeuralNetwork`

object `net3`

.

net3 = idNeuralNetwork(snet);

Use `lsqnonlin`

as the search method with a maximum of 20 iterations. Estimate the nonlinear ARX model with `net3`

as the output function using `nlarx`

.

```
opt.SearchMethod = "lsqnonlin";
opt.SearchOptions.MaxIterations = 20;
nlarx3 = nlarx(eData,lreg,net3,opt)
```

nlarx3 = Nonlinear ARX model with 1 output and 1 input Inputs: Torque Outputs: Angular Velocity Regressors: Linear regressors in variables Angular Velocity, Torque List of all regressors Output function: Shallow network of Deep Learning Toolbox Sample time: 0.005 seconds Status: Estimated using NLARX on time domain data "estimation data". Fit to estimation data: 75.56% (simulation focus) FPE: 1.084, MSE: 25.91 Model Properties

Check the model performance for infinite-step-ahead prediction on the validation data set by comparing the fit accuracies for the models `nlarx1`

, `nlarx2`

, and `nlarx3`

.

compare(vData,nlarx1,nlarx2,nlarx3)

### Hammerstein-Wiener Model Identification

Identify Hammerstein-Wiener models by using different configurations of neural networks for mapping the regressors to the output.

#### Use Regression Neural Network

Create a regression neural network object with one hidden layer of size 5 and one hidden layer of size 7 using `idNeuralNetwork`

. Specify the activation function of both the layers as `sigmoid`

.

net4 = idNeuralNetwork([5 7],"sigmoid",NetworkType="RegressionNeuralNetwork");

Estimate the Hammerstein-Wiener model with no input nonlinearity and `net4`

as the output nonlinearity using `nlhw`

. Specify the model order as `[3 5 0]`

based on the `selstruc`

analysis performed earlier. Here, `nf`

is similar to the `na`

order of an ARX model.

nb = 5; nf = 3; nk = 0; nlhw1 = nlhw(eData,[nb,nf,nk],[],net4)

nlhw1 = Hammerstein-Wiener model with 1 output and 1 input Linear transfer function corresponding to the orders nb = 5, nf = 3, nk = 0 Input nonlinearity: None Output nonlinearity: Regression neural network Sample time: 0.005 seconds Status: Estimated using NLHW on time domain data "estimation data". Fit to estimation data: 76.4% FPE: 25.9, MSE: 24.16 Model Properties

#### Use Deep Learning Network

Create a deep learning network object that contains one hidden layer of size 8, one hidden layer of size 5, and one hidden layer of size 3 using `idNeuralNetwork`

. Specify the activation function of all three layers as `leakyRelu`

.

net5 = idNeuralNetwork([8 5 3],"leakyRelu",NetworkType="dlnetwork");

Estimate the Hammerstein-Wiener model with no input nonlinearity and `net5`

as the output nonlinearity using `nlhw`

. Specify the same model order as before.

nlhw2 = nlhw(eData,[nb,nf,nk],[],net5)

nlhw2 = Hammerstein-Wiener model with 1 output and 1 input Linear transfer function corresponding to the orders nb = 5, nf = 3, nk = 0 Input nonlinearity: None Output nonlinearity: Deep learning network Sample time: 0.005 seconds Status: Estimated using NLHW on time domain data "estimation data". Fit to estimation data: 75.76% FPE: 27.96, MSE: 25.48 Model Properties

#### Use Shallow Network

Create a cascade-forward (shallow) network with one hidden layer of size 10 using `cascadeforwardnet`

(Deep Learning Toolbox).

net = cascadeforwardnet(10);

Incorporate the shallow network into the `idNeuralNetwork`

object `net6`

.

net6 = idNeuralNetwork(net);

Estimate the Hammerstein-Wiener model with no input nonlinearity and `net6`

as the output nonlinearity using `nlhw`

. Specify the same model order as before.

nlhw3 = nlhw(eData,[nb,nf,nk],[],net6)

nlhw3 = Hammerstein-Wiener model with 1 output and 1 input Linear transfer function corresponding to the orders nb = 5, nf = 3, nk = 0 Input nonlinearity: None Output nonlinearity: Shallow network of Deep Learning Toolbox Sample time: 0.005 seconds Status: Estimated using NLHW on time domain data "estimation data". Fit to estimation data: 76.98% FPE: 23.95, MSE: 22.98 Model Properties

Check the model performance for infinite-step-ahead prediction on the validation data set by comparing the fit accuracies for the models `nlhw1`

, `nlhw2`

, and `nlhw3`

.

compare(vData,nlhw1,nlhw2,nlhw3)

The plots in this example show that the black-box models created using neural networks have a good fit percentage and perform well for longer prediction horizons.

## See Also

`iddata`

| `arxstruc`

| `selstruc`

| `linearRegressor`

| `dlnetwork`

(Deep Learning Toolbox) | `feedforwardnet`

(Deep Learning Toolbox) | `cascadeforwardnet`

(Deep Learning Toolbox) | `fitrnet`

(Statistics and Machine Learning Toolbox) | `RegressionNeuralNetwork`

(Statistics and Machine Learning Toolbox) | `idNeuralNetwork`

| `nlhw`

| `nlarx`