Online wavelet least-squares support vector machine fuzzy predictive control for engine lambda regulation

Abstract

A novel online wavelet least-squares support machine fuzzy predictive control for engine lambda regulation is presented in this article. The prediction model of the proposed online wavelet least-squares support machine fuzzy predictive control is built and updated with a newly proposed modelling algorithm, namely, online wavelet least-squares support machines. The proposed online wavelet least-squares support machine adopts wavelet function that can inherit the local analysis ability and feature extraction from the wavelet transformation, as well as a novel online incremental and decremental updating procedure that can maintain the built prediction model to be accurate, sparse and updated without losing the generalization by continually adding the latest useful data and pruning out the outdated data. Besides, an advanced fuzzy optimizer is proposed to determine the optimal control signal for the online wavelet least-squares support machine fuzzy predictive control, which is faster than the traditional optimizers. The proposed online wavelet least-squares support machine fuzzy predictive control was implemented on a real performance test car and compared with the latest lambda control techniques based on various modelling algorithms, optimizers, updating procedure and support vector kernel for evaluating the effectiveness. The experimental results show that the proposed online wavelet least-squares support machine fuzzy predictive control is a promising scheme for lambda regulation.