Paper Info
Title
Nonlinear Model Predictive Control of A Gasoline HCCI Engine Using Extreme Learning Machines.
Abstract
Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with a high fuel efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates a model based control approach for automotive application. HCCI engine control is a nonlinear, multi-input multi-output problem with state and actuator constraints which makes controller design a challenging task. Typical HCCI controllers make use of a first principles based model which involves a long development time and cost associated with expert labor and calibration. In this paper, an alternative approach based on machine learning is presented using extreme learning machines (ELM) and nonlinear model predictive control (MPC). A recurrent ELM is used to learn the nonlinear dynamics of HCCI engine using experimental data and is shown to accurately predict the engine behavior several steps ahead in time, suitable for predictive control. Using the ELM engine models, an MPC based control algorithm with a simplified quadratic program update is derived for real time implementation. The working and effectiveness of the MPC approach has been analyzed on a nonlinear HCCI engine model for tracking multiple reference quantities along with constraints defined by HCCI states, actuators and operational limits.
Year
Venue
Field
2015
CoRR
Nonlinear system,Control theory,Model predictive control,Control engineering,Homogeneous charge compression ignition,Fuel efficiency,Quadratic programming,Mathematics,Calibration,Automotive industry,Actuator
DocType
Volume
Citations 
Journal
abs/1501.03969
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Vijay Manikandan Janakiraman1405.20
XuanLong Nguyen211.76
Dennis Assanis3363.78