Combustion Control of Diesel Engine using Feedback Error Learning with Kernel Online Learning Approach
Elfady Satya Widayaka, Hiromitsu Ohmori · Journal of Physics Conference Series · 2016
This paper shows how to design Multivariable Model Reference Adaptive Control System (MRACS) for "Tokyo University discrete-time engine model" proposed by Yasuda et al (2014). This controller configuration has the structure of "Feedback error learning (FEL)" and adaptive law is based on kernel method. Simulation results indicate that "kernelized" adaptive controllers can improve the tracking performance, the speed of convergence and the robustness to disturbances.