Model Representation of Multi-Cyclic Phenomena Using Role State Variables: Model Based Fast Idling Control of SI Engine

2008 ◽  
Vol 1 (4) ◽  
pp. 320-328 ◽  
Author(s):  
Tomohiko JIMBO ◽  
Yoshikazu HAYAKAWA
Keyword(s):  
Model Representation ◽  
State Variables ◽  
Si Engine ◽  
Model Based ◽  
Cyclic Phenomena

scholarly journals Model-based Evidence for Cyclic Phenomena in a High-Elevation, Two-Species Forest

Ecosystems ◽  
2015 ◽  
Vol 19 (3) ◽  
pp. 437-449 ◽  
Author(s):  
Adrianna C. Foster ◽  
Herman H. Shugart ◽  
Jacquelyn K. Shuman
Keyword(s):  
High Elevation ◽  
Model Based ◽  
Cyclic Phenomena

Physical-Model-Based Control of Engine Cold Start via Role State Variables

2008 ◽  
Vol 41 (2) ◽  
pp. 1024-1029 ◽  
Author(s):  
Jimbo Tomohiko ◽  
Hayakawa Yoshikazu
Keyword(s):  
Physical Model ◽  
Cold Start ◽  
State Variables ◽  
Model Based Control ◽  
Model Based

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

2013 ◽  
Vol 66 (6) ◽  
pp. 859-877 ◽  
Author(s):  
M. Malleswaran ◽  
V. Vaidehi ◽  
S. Irwin ◽  
B. Robin
Keyword(s):  
Kalman Filter ◽  
Constant Velocity ◽  
Unscented Kalman Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
State Variables ◽  
Constant Acceleration ◽  
Positional Accuracy ◽  
Model Based ◽  
Novel Approach

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

Enhanced Two-stage Ignition Delay Model Based on Molar Fraction of Fuel Components for SI Engine Simulation

2018 ◽  
Author(s):  
Kyoung Hyun Kwak ◽  
Dewey Jung ◽  
Hyunil Park ◽  
Jeonghwan Paeng ◽  
Kyumin Hwang
Keyword(s):  
Ignition Delay ◽  
Molar Fraction ◽  
Delay Model ◽  
Si Engine ◽  
Two Stage ◽  
Engine Simulation ◽  
Model Based ◽  
Fuel Components

scholarly journals Rate-, state-, and pressure-dependent friction model based on the elastoplastic theory

Friction ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 768-783 ◽  
Author(s):  
Shingo Ozaki ◽  
Takeru Matsuura ◽  
Satoru Maegawa
Keyword(s):  
Friction Force ◽  
Contact Area ◽  
Contact Surface ◽  
Internal State ◽  
Friction Model ◽  
Real Contact Area ◽  
State Variables ◽  
Real Contact ◽  
Model Based ◽  
Acrylic Plate

AbstractAdhesion is one of essences with respect to rubber friction because the magnitude of the friction force is closely related to the magnitude of adhesion on a real contact area. However, the real contact area during sliding depends on the state and history of the contact surface. Therefore, the friction force occasionally exhibits rate-, state-, and pressure dependency. In this study, to rationally describe friction and simulate boundary value problems, a rate-, state-, and pressure-dependent friction model based on the elastoplastic theory was formulated. First, the evolution law for the friction coefficient was prescribed. Next, a nonlinear sliding surface (frictional criterion) was adopted, and several other evolution laws for internal state variables were prescribed. Subsequently, the typical response characteristics of the proposed friction model were demonstrated, and its validity was verified by comparing the obtained results with those of experiments conducted considering the contact surface between a rough rubber hemisphere and smooth acrylic plate.

Model-based event-triggered adaptive formation control for underactuated surface vehicles via the minimal learning parameter technique

2021 ◽  
pp. 095965182110402
Author(s):  
Guoqing Zhang ◽  
Wei Yu ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Formation Control ◽  
Lyapunov Theory ◽  
Polar Coordinate System ◽  
State Variables ◽  
Model Based ◽  
Minimal Learning Parameter ◽  
Polar Coordinate ◽  
Learning Parameter ◽  
And Control ◽  
Event Triggered

This article presents an adaptive neural formation control algorithm for underactuated surface vehicles by the model-based event-triggered method. In the algorithm, the leader–follower structure is employed to construct the formation model. Meanwhile, two new coordinate variables are introduced to avoid the possible singularity problem that exists in the polar coordinate system. Furthermore, the event-triggered mechanism is developed by constructing the adaptive model in a concise form. Related state variables and control parameters are required to be updated only at the event-triggered instants. Thus, the communication load between the controller and the actuator could be effectively reduced. Besides, for merits of the radial basis function neural network and the minimal learning parameter techniques, only two adaptive parameters are employed to compensate for the effects of the model uncertainties and the external disturbances. With the Lyapunov theory, all signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded. Finally, numerical simulations are conducted to illustrate the effectiveness and feasibility of the proposed algorithm.

scholarly journals A generalized observation confirmation model to account for false positive error in species detection-nondetection data

2018 ◽  
Author(s):  
John D. J. Clare ◽  
Benjamin Zuckerberg ◽  
Philip A. Townsend
Keyword(s):  
Relative Abundance ◽  
False Positive ◽  
False Positives ◽  
State Variables ◽  
Occupancy Models ◽  
Positive Error ◽  
Imperfect Detection ◽  
False Positive Error ◽  
Model Based ◽  
Sampling Protocols

AbstractSpatially-indexed repeated detection-nondetection data is widely collected by ecologists interested in estimating parameters associated with species distribution, relative abundance, phenology, and more while accounting for imperfect detection. Recent model development has focused on accounting for false positive error as well, given growing recognition that misclassification is common across many sampling protocols. To date, however, the development of model-based solutions to false positive error has been largely restricted to occupancy models. We describe a general form of the observation confirmation protocol originally described for occupancy estimation that permits investigators to flexibly and intuitively extend several models for detection-nondetection data to account for false positive error. Simulation results demonstrate that estimators for relative abundance and arrival time exhibit relative bias greater than 20% under realistic levels of false positive prevalence (e.g., 5% of detections are false positive). Bias increases as true and false positives occur in more distinct places or times, but can also be sensitive to the values of the state variables of interest, sampling design, and sampling efficiency. Results from an empirical study focusing on patterns of gray fox relative abundance across Wisconsin, USA suggest that false positive error can also distort estimated spatial patterns often used to guide decision-making. The extended estimators described within typically improve performance at any level of confirmation, and when false positive error occurs at random and constitutes less than 10% of all detections, the estimators are essentially unbiased when more than 50 observations can be confirmed as true or false positives. The generalized form of the observation-confirmation protocol is a flexible model-based solution to false positive error useful for researchers collecting data with sampling devices like trail or smartphone cameras, acoustic recorders, or other techniques where classifications can be reviewed post-hoc.

A model-based SI engine air fuel ratio controller

2006 ◽  
Author(s):  
K.R. Muske
Keyword(s):  
Si Engine ◽  
Fuel Ratio ◽  
Model Based
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