Application of adaptive neural predictive control for an automotive air conditioning system

Abstract

In this paper, a Model Predictive Controller (MPC) using an online trained artificial neural network (ANN) as the nonlinear plant model is implemented for an automotive air conditioning (AAC) system equipped with a variable speed compressor (VSC). The training scheme using Levenberg–Marquardt algorithm and sliding stack window technique is incorporated to train the ANN model in real time so that the time varying dynamics of the AAC system can be captured throughout the control process. The ANN model is initially identified offline using the training and testing data obtained from the experimental AAC system. Validation of the neural network is performed using one-step-ahead and 10-steps-ahead prediction tests. Subsequently, several experimental tests are carried out on the AAC test bench to verify the capability of the proposed controller in tracking set point changes and rejecting disturbances. In order to show the advantages of incorporating an online trained ANN in the proposed controller, comparative assessment is performed between the proposed adaptive controller and two other control schemes, namely a MPC using an offline trained ANN model and a conventional PID controller. The experimental results signify the superiority of the proposed control scheme in terms of reference tracking as well as disturbance rejection due to its adaptation capability in capturing the real time AAC system behaviour over the wide range of operation conditions.