Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

scholarly journals Hand interception of occluded motion in humans: a test of model-based vs. on-line control

2015 ◽  
Vol 114 (3) ◽  
pp. 1577-1592 ◽  
Author(s):  
Barbara La Scaleia ◽  
Myrka Zago ◽  
Francesco Lacquaniti
Keyword(s):  
Visual Information ◽  
Target Motion ◽  
Physical Models ◽  
Model Based Control ◽  
Projectile Motion ◽  
Starting Position ◽  
Model Based ◽  
Model Free ◽  
Ball Velocity ◽  
On Line

Two control schemes have been hypothesized for the manual interception of fast visual targets. In the model-free on-line control, extrapolation of target motion is based on continuous visual information, without resorting to physical models. In the model-based control, instead, a prior model of target motion predicts the future spatiotemporal trajectory. To distinguish between the two hypotheses in the case of projectile motion, we asked participants to hit a ball that rolled down an incline at 0.2 g and then fell in air at 1 g along a parabola. By varying starting position, ball velocity and trajectory differed between trials. Motion on the incline was always visible, whereas parabolic motion was either visible or occluded. We found that participants were equally successful at hitting the falling ball in both visible and occluded conditions. Moreover, in different trials the intersection points were distributed along the parabolic trajectories of the ball, indicating that subjects were able to extrapolate an extended segment of the target trajectory. Remarkably, this trend was observed even at the very first repetition of movements. These results are consistent with the hypothesis of model-based control, but not with on-line control. Indeed, ball path and speed during the occlusion could not be extrapolated solely from the kinematic information obtained during the preceding visible phase. The only way to extrapolate ball motion correctly during the occlusion was to assume that the ball would fall under gravity and air drag when hidden from view. Such an assumption had to be derived from prior experience.

Model-Based Control of Combustion Instabilities

2005 ◽  
Author(s):  
Aimee S. Morgans ◽  
Ann P. Dowling
Keyword(s):  
Transfer Function ◽  
Controller Design ◽  
Open Loop ◽  
Operating Conditions ◽  
Combustion Instabilities ◽  
Lean Premixed Combustion ◽  
Model Based Control ◽  
Model Based ◽  
Loop Transfer Function ◽  
Mathematical Approximation

Model-based control has been successfully implemented on an atmospheric pressure lean premixed combustion rig. The rig incorporated a pressure transducer in the combustor to provide a sensor measurement, with actuation provided by a fuel valve. Controller design was based on experimental measurement of the open loop transfer function. This was achieved using a valve input signal which was the sum of an identification signal and a control signal from an empirical controller to eliminate the non-linear limit cycle. The transfer function was measured for the main instability occurring at a variety of operating conditions, and was found to be fairly similar in all cases. Using Nyquist and H∞-loop shaping techniques, several robust controllers were designed, based on a mathematical approximation to the measured transfer function. These were implemented experimentally on the rig, and were found to stabilise it under a variety of operating conditions, with a greater reduction in the pressure spectrum than had been achieved by the empirical controller.

Mechanics and Model Based Control

2012 ◽  
Vol 83 ◽  
pp. 85-94
Author(s):  
Hans Irschik ◽  
Michael Krommer ◽  
Kurt Schlacher
Keyword(s):  
Solid Mechanics ◽  
Physical Models ◽  
Distributed Parameter ◽  
Science Park ◽  
Present Contribution ◽  
Model Based Control ◽  
Johannes Kepler ◽  
Model Based ◽  
The University ◽  
And Control

The present contribution gives an overview on own research that has been performed from 2008 to 2011 in Area 2, Mechanics and Model Based Control, of the COMET K2 Austrian Center of Competence in Mechatronics (ACCM), which is situated at the Science Park of the Johannes Kepler University of Linz. Area 2 is motivated by the fact that mechanics and control both are rapidly expanding scientific fields, which share demanding mathematical and/or system-theoretic formulations and methods. The goal of Area 2 has been to utilize and extend these relations, with special emphasis on solid mechanics and control methods based on physical models. Some corresponding results will be reviewed subsequently with respect to the mechanical modelling of structures, robots and machines, and with respect to the corresponding model based control as linear/non-linear lumped/distributed parameter systems. Due to limitations in space, the present review restricts itself to work of Area 2 that has been directly performed at the University of Linz. The review contains 118 references to works on mechanics and model based control.

A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings

2011 ◽  
Vol 90-93 ◽  
pp. 3061-3067
Author(s):  
Hai Tao Wang ◽  
You Ming Chen ◽  
Cary W.H. Chan ◽  
Jian Ying Qin
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
Office Buildings ◽  
Model Parameters ◽  
Time Data ◽  
Model Based ◽  
Air Handling Units ◽  
Detection And Diagnosis

The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.

scholarly journals Investigation on the Model-Based Control Performance in Vehicle Safety Critical Scenarios with Varying Tyre Limits

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5372
Author(s):  
Aleksandr Sakhnevych ◽  
Vincenzo Maria Arricale ◽  
Mattia Bruschetta ◽  
Andrea Censi ◽  
Enrico Mion ◽  
...  
Keyword(s):  
Rapid Development ◽  
Operating Conditions ◽  
Model Based Control ◽  
The Road ◽  
Automotive Market ◽  
Model Based ◽  
Entire Vehicle ◽  
Minimum Requirements ◽  
Available Information ◽  
Virtual Driver

In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safetycritical situations, where the environmental conditions (rain, snow, ice), the road singularities (oil stains, puddles, holes), and the tyre thermal and ageing phenomena can deeply affect the adherence potential. The objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm in a hardware-in-the-loop real-time environment and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. The multidisciplinary proposal will provide a paradigm shift in the development of strategies and a solid breakthrough towards enhanced development of the driving automatization systems, unleashing the potential of physical modeling to the next level of vehicle control, able to exploit and to take into account the multi-physical tyre variations.

Study On Design Method of Combustion Reference Values for Model-Based Control of Advanced Diesel Engine

2021 ◽  
Author(s):  
Jihoon Kim ◽  
Yudai Yamasaki
Keyword(s):  
Heat Release ◽  
Control Systems ◽  
Reference Values ◽  
Heat Release Rate ◽  
Release Rate ◽  
Design Method ◽  
Operating Conditions ◽  
Compression Ignition ◽  
Model Based Control ◽  
Model Based

Abstract Model-based control systems are drawing attention in relation to implementing next-generation combustion technologies with high thermal efficiency and low emissions, such as homogeneous charge compression ignition (HCCI) and premixed charge compression ignition (PCCI) combustion, which have low robustness. A model-based control system derives control inputs according to reference values and operating conditions during every cycle, and has potential to replace the conventional control map, which requires a large number of experiments. However, model-based control for engines requires reference values for combustion, such as heat release rate peak timing and heat release rate peak value; such values represent the combustion state. Therefore, the reference for the transient condition is important for utilizing the benefit of model-based control systems, given that such systems derive control outputs cycle by cycle. In this study, design method for the combustion reference values for the transient operating condition is described for advanced diesel combustion, which uses premixed compression ignition combustion shows multiple heat releases. Specifically, a method utilizing future operating conditions in consideration of the driving characteristics is proposed and compared in engine control experiments. The proposed method was evaluated under certain part of worldwide harmonized light vehicles test cycles (WLTC) mode considering real road conditions. Results showed that designing the combustion reference values for transient operation by considering future operating conditions is effective to ensure advanced combustion, and such method has the potential to consider the driving characteristics.

Online Automatic Adaptation for Model-based Control of Diesel Engine

2019 ◽  
Author(s):  
Jianan CAO ◽  
Motoki TAKAHASHI ◽  
Yudai YAMASAKI ◽  
Shigehiko KANEKO
Keyword(s):  
Diesel Engine ◽  
Model Based Control ◽  
Model Based ◽  
Automatic Adaptation

Study on Design Method of Combustion Reference Values for Model-Based Control of Advanced Diesel Engine

2020 ◽  
Author(s):  
Jihoon Kim ◽  
Yudai Yamasaki
Keyword(s):  
Heat Release ◽  
Control Systems ◽  
Reference Values ◽  
Model Prediction ◽  
Design Method ◽  
Operating Conditions ◽  
Compression Ignition ◽  
Transient Operation ◽  
Model Based Control ◽  
Model Based

Abstract Model-based control systems are drawing attention in relation to implementing next-generation combustion technologies with high thermal efficiency and low emissions, such as homogeneous charge compression ignition (HCCI) and premixed charge compression ignition (PCCI) combustion, which have low robustness. A model-based control system derives control inputs according to reference values and operating conditions during every cycle and has potential to replace the conventional control map, which requires a large number of experiments. However, model-based control for engines requires reference values for combustion, such as heat release peak timing and heat release peak value; such values represent the combustion state. Therefore, the reference for the transient condition is important for utilizing the benefit of model-based control systems, given that such systems derive control outputs cycle by cycle. In this study, a design method for the combustion reference values for the transient operating condition is described for advanced diesel combustion, which uses premixed compression ignition combustion by multiple fuel injections. Specifically, a statistical method and a method based on model prediction considering the driving characteristics are proposed and compared in engine control experiments. These proposed methods were evaluated under defined simple transient operation conditions and worldwide harmonized light vehicles test cycles (WLTC) mode considering real road conditions. Results showed that designing the combustion reference values for transient operation by model prediction is effective, and such method has the potential to reflect the driving characteristics.

scholarly journals Model Based Control with Online Automatic Adaptation by Neural Network for Advanced Diesel Combustion

2020 ◽  
Vol 53 (2) ◽  
pp. 14040-14046
Author(s):  
Jianan Cao ◽  
Jihoon Kim ◽  
Motoki Takahashi ◽  
Yudai Yamasaki
Keyword(s):  
Neural Network ◽  
Diesel Combustion ◽  
Model Based Control ◽  
Model Based ◽  
Automatic Adaptation

Model-Based Control of the Air Fuel Ratio for Gasoline Direct Injection Engines via Advanced Co-Simulation: An Approach to Reduce the Development Cycle of Engine Control Systems

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Alessandro di Gaeta ◽  
Umberto Montanaro ◽  
Veniero Giglio
Keyword(s):  
Control Systems ◽  
Control Strategy ◽  
Direct Injection ◽  
Gasoline Direct Injection ◽  
Model Parameters ◽  
Air Mass ◽  
Model Based Control ◽  
Fuel Ratio ◽  
Model Based ◽  
Wide Range

Nowadays, the precise control of the air fuel ratio (AFR) in spark ignition (SI) engines plays a crucial role in meeting the more and more restrictive standard emissions for the passenger cars and the fuel economy required by the automotive market as well. To attain this demanding goal, the development of an advanced AFR control strategy embedding highly predictive models becomes mandatory for the next generation of electronic control unit (ECU). Conversely, the adoption of more complex control strategies affects the development time of the ECU increasing the time-to-market of new engine models. In this paper to solve the AFR control problem for gasoline direct injection (GDI) and to speed up the design of the entire control system, a gain scheduling PI model-based control strategy is proposed. To this aim, AFR dynamics are modeled via a first order time delay system whose parameters vary strongly with the fresh air mass entering the cylinders. Nonlinear relations have been found to describe the behavior of model parameters in function of air mass. Closed loop performances, when this novel controller is nested in the control loop, are compared to those provided by the classical PI Ziegler–Nichols control action with respect to different cost functions. Model validation as well as the effectiveness of the control design are carried out by means of ECU-1D engine co-simulation environment for a wide range of engine working conditions. The combination in one integrated designing environment of control systems and virtual engine, simulated through high predictive commercial one dimensional code, becomes a high predictive tool for automotive control engineers and enables fast prototyping.

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