Dynamic modelling of an automotive variable speed air conditioning system using nonlinear autoregressive exogenous neural networks

Abstract In this paper, a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed to model the dynamic behaviour of an automotive air conditioning (AAC) system equipped with a variable speed compressor. Two popular network architectures of artificial neural network namely multilayer perceptron (MLP) and radial basis network (RBN) are adopted to simulate the transient cabin temperature under random modulation of compressor speed. The input and output data required for the system identification are collected from an experimental bench made up of the original components of an AAC system. Optimization of network structure is conducted for the respective network architecture to study the effect of model structures on the model predictive performance. One-Step-Ahead (OSA) and Model Predicted Output (MPO) prediction tests are used to validate and verify the model. Investigation results show that NARX model with the investigated network architectures can be a powerful tool in identifying the nonlinear AAC system. However, a comparison between them signifies the advantage of MLP model over RBN model in term of network complexity, generalization capability and robustness against the presence of noise in the training data. The identified model of the AAC system can be used for further implementation in a model based intelligent control of an AAC system equipped with a variable speed compressor.