Application of back-propagation neural networks to the modeling and control of multiple-input, multiple-output processes

Abstract

Certain properties of back-propagation neural networks have been found to be useful in structuring models for multiple-input, multiple-output (MIMO) processes. The network's simplicity and its ability to identify the non-linearity can have wide impacts on the construction of model-based control system. Care must be taken to train the network with consistent data that contains sufficient dynamic information. A predictive control system based on the network model was proposed. Although the controller is relatively simple in terms of concept and computation, it shows excellent performances both in servo and regulator problems. Model prediction error sometimes causes a cyclic behavior in process responses; however, it can be stabilized by imposing certain constraints of controller action. The constraints are also effective for noisy measurements. Use of neural networks for modeling and control of MIMO system appears to be very promising with its ability to treat non-linearity and process interactions