aDepartment of Building Services Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
bDepartment of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom
Abstract:
This paper describes a new method for increasing the computational efficiency of nonlinear robust model-based predictive control. It is based on the application of neuro-fuzzy networks and improves the computation efficiency by arranging the online optimisation to be done offline. The offline optimisation is realized by offline training a neuro-fuzzy network, consisting of zero-order T–S fuzzy rules, which is designed to approximate the input–output relationship of a robust model-based predictive controller. The design and the training of the neuro-fuzzy network are described, and the corresponding control algorithm is developed. Experiment results performed on the temperature control loop of an experimental air-handling unit (AHU) demonstrate the effectiveness of this approach.