Neural Network Toolbox

Predictive Control

The model predictive control method is based on the receding horizon technique [SoHa96]. The neural network model predicts the plant response over a specified time horizon. The predictions are used by a numerical optimization program to determine the control signal that minimizes the following performance criterion over the specified horizon.

where , and define the horizons over which the tracking error and the control increments are evaluated. The variable is the tentative control signal, is the desired response and is the network model response. The value determines the contribution that the sum of the squares of the control increments has on the performance index.

The following block diagram illustrates the model predictive control process. The controller consists of the neural network plant model and the optimization block. The optimization block determines the values of that minimize , and then the optimal is input to the plant. The controller block has been implemented in Simulink, as described in the following section.

System Identification

Using the NN Predictive Controller Block

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