Neural Network Toolbox

Model Reference Control

The neural model reference control architecture uses two neural networks: a controller network and a plant model network, as shown in the following figure. The plant model is identified first, and then the controller is trained so that the plant output follows the reference model output.

The figure on the following page shows the details of the neural network plant model and the neural network controller, as they are implemented in the Neural Network Toolbox. Each network has two layers, and you can select the number of neurons to use in the hidden layers. There are three sets of controller inputs:

• Delayed reference inputs
• Delayed controller outputs
• Delayed plant outputs

For each of these inputs, you can select the number of delayed values to use. Typically, the number of delays increases with the order of the plant. There are two sets of inputs to the neural network plant model:

• Delayed controller outputs
• Delayed plant outputs

As with the controller, you can set the number of delays. The next section demonstrates how you can set the parameters.

Using the NARMA-L2 Controller Block

Using the Model Reference Controller Block

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