scholarly journals Intake Air Mass Observer Design Based on Extended Kalman Filter for Air-Fuel Ratio Control on SI Engine

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3444
Author(s):  
Lei Meng ◽  
Jie Luo ◽  
Xu Yang ◽  
Chunnian Zeng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Catalytic Converter ◽  
Random Noise ◽  
Observer Design ◽  
Exhaust Emission ◽  
Si Engine ◽  
Air Mass ◽  
Fuel Ratio ◽  
Experiment Validation

Air-fuel ratio (AFR) control is important for the exhaust emission reduction while using the three-way catalytic converter in the spark ignition (SI) engine. However, the transient cylinder air mass is unable to acquire by sensors directly and it may limit the accuracy of AFR control. The complex engine dynamics and working conditions make the intake air estimation a challenge work. In this paper, a novelty design of intake air observer is investigated for the port-injected SI engine. The intake air dynamical modeling and the parameter fitting have been carried out in detail. Extended Kalman Filter (EKF) has been used to optimize the instantaneous cylinder charge estimation and minimize the effort of pump gas fluctuation, random noise, and measurement noise. The experiment validation has been conducted to verify the effectiveness of the proposed method.

scholarly journals Adaptive Air-Fuel Ratio Regulation for Port-Injected Spark-Ignited Engines Based on a Generalized Predictive Control Method

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 173 ◽  
Author(s):  
Lei Meng ◽  
Xiaofeng Wang ◽  
Chunnian Zeng ◽  
Jie Luo
Keyword(s):  
Predictive Control ◽  
Noise Suppression ◽  
Control Method ◽  
Simulation Analysis ◽  
Catalytic Converter ◽  
Exhaust Emission ◽  
Generalized Predictive Control ◽  
Si Engine ◽  
Fuel Ratio ◽  
Wall Wetting

The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application.

Dynamic Sliding Mode Control of Air-to-Fuel Ratio in Internal Combustion Engines Using the Hybrid Extended Kalman Filter

2012 ◽  
Vol 433-440 ◽  
pp. 2092-2098 ◽  
Author(s):  
Majid Zohari ◽  
Mohamadreza Ahmadi ◽  
Hamed Mojallali
Keyword(s):  
Kalman Filter ◽  
Sliding Mode Control ◽  
Extended Kalman Filter ◽  
Sliding Mode ◽  
Nonlinear Observer ◽  
Superior Performance ◽  
Fuel Ratio ◽  
Mode Control ◽  
Dynamic Sliding Mode Control ◽  
Dynamic Sliding Mode

The large modeling uncertainties and the nonlinearities associated with air manifold and fuel injection in spark ignition (SI) engines has given rise to difficulties in the task of designing an adequate controller for air-to-fuel ratio (AFR) control. Although sliding mode control approaches has been suggested, the inescapable time-delay between control action and measurement update results in chattering. This paper proposes the implementation of a nonlinear observer based control scheme incorporating the hybrid extended Kalman filter (HEKF) and the dynamic sliding mode control (DSMC). The results established upon the proposed methodology are given which demonstrate superior performance in terms of reducing the chattering magnitude.

Influence of Air/Fuel Ratio on Cyclic Variation and Exhaust Emission in Natural Gas SI Engine

1999 ◽  
Author(s):  
Livier Ben ◽  
Nathalie Raud-Ducros ◽  
Remy Truquet ◽  
Georges Charnay
Keyword(s):  
Natural Gas ◽  
Exhaust Emission ◽  
Cyclic Variation ◽  
Si Engine ◽  
Fuel Ratio

Invariant observer design for scan matching-aided localization: A tutorial

2018 ◽  
Vol 66 (3) ◽  
pp. 195-212
Author(s):  
Martin Barczyk
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Design Methodology ◽  
Mobile Robotics ◽  
Filter Design ◽  
Observer Design ◽  
Nonlinear Observer ◽  
The Novel ◽  
Scan Matching ◽  
Filter Approach

Abstract Aided localization, a nonlinear observer design problem, is critical to a range of mobile robotics applications. This paper provides a tutorial presentation of the required background, design steps and calculations of a scan matching-aided localization observer using the novel invariant (symmetry-preserving) observer design methodology. The Invariant Extended Kalman Filter approach is used to systematically obtain the gains of the resulting design, and compared against a conventional, non-invariant Extended Kalman Filter design.

scholarly journals Contact-aided invariant extended Kalman filtering for robot state estimation

2020 ◽  
Vol 39 (4) ◽  
pp. 402-430 ◽  
Author(s):  
Ross Hartley ◽  
Maani Ghaffari ◽  
Ryan M Eustice ◽  
Jessy W Grizzle
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Domain Of Attraction ◽  
Observer Design ◽  
Measurement Unit ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Contact Points ◽  
Error Dynamics ◽  
Log Linear

Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). In this work, we develop a contact-aided invariant extended Kalman filter (InEKF) using the theory of Lie groups and invariant observer design. This filter combines contact-inertial dynamics with forward kinematic corrections to estimate pose and velocity along with all current contact points. We show that the error dynamics follows a log-linear autonomous differential equation with several important consequences: (a) the observable state variables can be rendered convergent with a domain of attraction that is independent of the system’s trajectory; (b) unlike the standard EKF, neither the linearized error dynamics nor the linearized observation model depend on the current state estimate, which (c) leads to improved convergence properties and (d) a local observability matrix that is consistent with the underlying nonlinear system. Furthermore, we demonstrate how to include IMU biases, add/remove contacts, and formulate both world-centric and robo-centric versions. We compare the convergence of the proposed InEKF with the commonly used quaternion-based extended Kalman filter (EKF) through both simulations and experiments on a Cassie-series bipedal robot. Filter accuracy is analyzed using motion capture, while a LiDAR mapping experiment provides a practical use case. Overall, the developed contact-aided InEKF provides better performance in comparison with the quaternion-based EKF as a result of exploiting symmetries present in system.

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