Neural Network Toolbox

NN Predictive Control

The neural network predictive controller that is implemented in the Neural Network Toolbox uses a neural network model of a nonlinear plant to predict future plant performance. The controller then calculates the control input that will optimize plant performance over a specified future time horizon. The first step in model predictive control is to determine the neural network plant model (system identification). Next, the plant model is used by the controller to predict future performance. (See the Model Predictive Control Toolbox documentation for a complete coverage of the application of various model predictive control strategies to linear systems.)

The following section describes the system identification process. This is followed by a description of the optimization process. Finally, it discusses how to use the model predictive controller block that has been implemented in Simulink®.

Introduction

System Identification

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