scholarly journals Robust Control of the Air to Fuel Ratio in Spark Ignition Engines with Delayed Measurements from a UEGO Sensor

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Javier Espinoza-Jurado ◽  
Emmanuel Dávila ◽  
Jorge Rivera ◽  
Juan José Raygoza-Panduro ◽  
Susana Ortega
Keyword(s):  
Oxygen Sensor ◽  
Sliding Mode ◽  
Time Derivative ◽  
Spark Ignition ◽  
Mean Value ◽  
Control Input ◽  
Spark Ignition Engines ◽  
Precise Control ◽  
Engine Model ◽  
Fuel Ratio

A precise control of the normalized air to fuel ratio in spark ignition engines is an essential task. To achieve this goal, in this work we take into consideration the time delay measurement presented by the universal exhaust gas oxygen sensor along with uncertainties in the volumetric efficiency. For that purpose, observers are designed by means of a super-twisting sliding mode estimation scheme. Also two control schemes based on a general nonlinear model and a similar nonlinear affine representation for the dynamics of the normalized air to fuel ratio were designed in this work by using the super-twisting sliding mode methodology. Such dynamics depends on the control input, that is, the injected fuel mass flow, its time derivative, and its reciprocal. The two latter terms are estimated by means of a robust sliding mode differentiator. The observers and controllers are designed based on an isothermal mean value engine model. Numeric and hardware in the loop simulations were carried out with such model, where parameters were taken from a real engine. The obtained results show a good output tracking and rejection of disturbances when the engine is closed loop with proposed control methods.

Air-Fuel Ratio Control of Spark Ignition Engines With TWC Using LPV Techniques

2009 ◽  
Author(s):  
Rohit A. Zope ◽  
Javad Mohammadpour ◽  
Karolos M. Grigoriadis ◽  
Matthew Franchek
Keyword(s):  
Design Method ◽  
Spark Ignition ◽  
Operating Conditions ◽  
System Stability ◽  
Linear Matrix ◽  
Pi Control ◽  
Spark Ignition Engines ◽  
Si Engine ◽  
Precise Control ◽  
Fuel Ratio

Precise control of the air-fuel ratio in a spark ignition (SI) engine is important to minimize emissions. The emission reduction strongly depends on the performance of the air-fuel ratio controller for the SI engine in conjunction with the Three Way Catalytic (TWC) converter. The TWC converter acts as a buffer to any variations occurring in the air-fuel ratio. It stores oxygen during a lean operation and releases the stored oxygen during a rich transient phase. The stored oxygen must be maintained close to the current storage capacity to yield maximum benefits from the TWC converter. Traditionally this is achieved using a simple PI control or a gain-scheduled PI control to address the variability in the operating conditions of the engine. This, however, does not guarantee closed-loop system stability and/or performance. In this work a model-based linear parameter varying (LPV) approach is used to design an H∞ controller. The design goal is to minimize the effect of disturbances on the air-fuel ratio and hence the relative storage level of oxygen in the TWC, over a defined operating range for the SI engine. The design method formulated in terms of Linear Matrix Inequalities (LMIs) leads to a convex optimization problem which can be efficiently solved using existing interior-point optimization algorithms. Simulations performed validate the proposed control design methodology.

scholarly journals NONLINEAR TORQUE AND AIR-TO-FUEL RATIO CONTROL OF SPARK IGNITION ENGINES USING NEURO-SLIDING MODE TECHNIQUES

2011 ◽  
Vol 21 (03) ◽  
pp. 213-224 ◽  
Author(s):  
TING HUANG ◽  
HOSSEIN JAVAHERIAN ◽  
DERONG LIU
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Spark Ignition ◽  
Spark Ignition Engines ◽  
New Approach ◽  
Fuel Ratio ◽  
Mode Control ◽  
Equivalent Control ◽  
Corrective Control

This paper presents a new approach for the calibration and control of spark ignition engines using a combination of neural networks and sliding mode control technique. Two parallel neural networks are utilized to realize a neuro-sliding mode control (NSLMC) for self-learning control of automotive engines. The equivalent control and the corrective control terms are the outputs of the neural networks. Instead of using error backpropagation algorithm, the network weights of equivalent control are updated using the Levenberg-Marquardt algorithm. Moreover, a new approach is utilized to update the gain of corrective control. Both modifications of the NSLMC are aimed at improving the transient performance and speed of convergence. Using the data from a test vehicle with a V8 engine, we built neural network models for the engine torque (TRQ) and the air-to-fuel ratio (AFR) dynamics and developed NSLMC controllers to achieve tracking control. The goal of TRQ control and AFR control is to track the commanded values under various operating conditions. From simulation studies, the feasibility and efficiency of the approach are illustrated. For both control problems, excellent tracking performance has been achieved.

An adaptive sliding mode controller for air-fuel ratio control of spark ignition engines

2001 ◽  
Vol 215 (2) ◽  
pp. 305-315 ◽  
Author(s):  
P Yoon ◽  
M Sunwoo
Keyword(s):  
Control Algorithm ◽  
Oxygen Sensor ◽  
Fuel Injection ◽  
Sliding Mode ◽  
Model Parameters ◽  
Model Errors ◽  
Engine Model ◽  
Fuel Ratio ◽  
Adaptive Sliding Mode Controller ◽  
Measurement Delay

An adaptive dynamic sliding mode fuel injection control algorithm based on the measurement of a binary oxygen sensor to reduce the exhaust gas emissions is proposed. The controller suggested in this paper is designed on the basis of the two-state dynamic engine model developed in the crank angle domain, and it is composed of an adaptation law for fuel delivery model parameters and measurement bias in mass air flowrate. The control algorithm is mathematically compact enough to run in real time, and it is robust to modelling errors as well as to rapidly changing manoeuvres of the throttle. The simulation and experimental results show that this algorithm can substantially reduce the transient peaks in air-fuel ratio (AFR) while maintaining robustness to model errors and measurement delay.

In-Cylinder Air Fuel Ratio and Combustion Control for Spark Ignition Engines

1995 ◽  
Author(s):  
Minoru Ohsuga ◽  
Jun'ichi Yamaguchi ◽  
Ryuhei Kawabe ◽  
Masakichi Momono
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Combustion Control ◽  
Fuel Ratio
Sign in / Sign up

Export Citation Format

Share Document