Development of a Friction Component Model for Automotive Powertrain System Analysis and Shift Controller Design based on Parallel-Modulated Neural Networks

2004 ◽  
Vol 127 (3) ◽  
pp. 382-405 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Automatic Transmission ◽  
Analytical Data ◽  
Operating Conditions ◽  
Component Model ◽  
System Model ◽  
Powertrain System ◽  
Friction Component ◽  
Wide Range

In this study, a new hybrid-neural-network-based friction component model is developed for powertrain (PT) dynamic analysis and controller design. This new model, with significantly improved input-output scalability over conventional neural network configuration, has the capability to serve as a forward as well as an inverse system model. The structural information of the available physical and empirical correlations is utilized to construct a parallel-modulated neural network (PMNN) architecture consisting of small parallel sub-networks reflecting specific mechanisms of the friction component engagement process. The PMNN friction component model isolates the contribution of engagement pressure on engagement torque while identifying the nonlinear characteristics of the pressure-torque correlation. Furthermore, it provides a simple torque formula that is scalable with respect to engagement pressure. The network is successfully trained, tested and analyzed, first using analytical data at the component level and then using experimental data measured in a transmission system. The PMNN friction component model, together with a comprehensive powertrain model, is implemented to simulate the shifting process of an automatic transmission (AT) system under various operating conditions. Simulation results demonstrate that the PMNN model can be effectively applied as a part of powertrain system model to accurately predict transmission shift dynamics. A pressure-profiling scheme using a quadratic polynomial pressure-torque relationship of the PMNN model is developed for transmission shift controller design. The results illustrate that the proposed pressure profiling technique can be applied to a wide range of operating conditions. This study demonstrates the potential of the PMNN architecture as a new dynamic system-modeling concept: It not only outperforms the conventional network modeling techniques in accuracy and numerical efficiency, but also provides a new tool for transmission controller design to improve shift quality.

Automatic Transmission Shift Calibration Based on Parallel-Modulated Neural Network (PMNN) Friction Component Model

2004 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Automatic Transmission ◽  
Applied Pressure ◽  
Operating Conditions ◽  
Component Model ◽  
Powertrain System ◽  
Friction Component ◽  
Wide Range ◽  
Shift Dynamics

The parallel-modulated-neural-network (PMNN) -based friction component model [19] provides a simple pressure-torque formula, which possesses much improved scalability with respect to the applied pressure. In this paper, the PMNN friction component model is implemented within a comprehensive powertrain model, to simulate the shifting process of an automatic transmission (AT) system under various operating conditions. Simulation results demonstrate that the PMNN model can be effectively applied as a part of powertrain system model to accurately predict transmission shift dynamics. A pressure-profiling scheme through a quadratic polynomial pressure-torque relationship from the PMNN model is developed for the transmission shifting optimization. This scheme is implemented to improve the transmission shifting quality under certain operating conditions. The pressure profiling results illustrate that the proposed pressure profiling technique can be potentially applied to a wide range of operating conditions. This study demonstrates that the PMNN architecture not only outperforms the conventional network modeling techniques in accuracy and numerical efficiency, but is also a new tool for AT controller design.

Parallel-Modulated Neural Network Friction Component Model for Automotive Powertrain Systems

2003 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Structural Information ◽  
Analytical Data ◽  
Component Model ◽  
Hybrid Network ◽  
Powertrain System ◽  
Friction Component ◽  
Engagement Process ◽  
Automotive Powertrain

In this paper, a new hybrid neural network friction component model is developed for dynamic powertrain (PT) analysis. With improvement over the previous hybrid network modeling techniques, the structural information of the available physical and empirical correlations is utilized to construct a parallel-modulated neural network (PMNN) architecture. The new neural network consists of small-sized parallel subnetworks reflecting specific mechanisms of the friction component engagement process. The network is successfully trained and tested using both experimental and analytical data. The PMNN-based friction component model isolates the contribution of engagement pressure on engagement torque while identifying the nonlinear characteristics of the pressuretorque correlation. Furthermore, it provides a simple torque formula that is scalable with respect to engagement pressure. All these features make the PMNN model a better tool for the purpose of powertrain system simulation and controller design, as compared to a conventional neural network model.

Neural Network Based Automotive Clutch Model for Dynamic Engagement Analysis With Variable Time Steps

2000 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Automatic Transmission ◽  
Operating Conditions ◽  
Hybrid Network ◽  
Time Step ◽  
Neural Network Approach ◽  
Time Pattern ◽  
Variable Time ◽  
Powertrain System ◽  
Wide Range

Abstract The lubricated clutch in an automatic transmission plays an important role in the performance and comfort of passenger vehicles. Therefore, an accurate and easy-to-implement dynamic clutch model is necessary for powertrain system design and performance studies. A neural network approach recently developed by Parvataneni et al. [1999] for clutch modeling has illustrated some very promising results. However, this model has complex architecture that may cause slow training and testing. Also, due to the lack of time information, the network cannot adapt to time step variations. Therefore, it cannot be easily integrated with powertrain system models, which in general require variable time steps for a superior numerical integration performance. In this paper, a new first-principle-based hybrid network clutch model is derived for dynamic engagement analysis with variable time steps. With improvement over the previous work by Parvataneni et al. [1999], the time pattern information is added to the inputs and a simpler architecture is developed through more explicit utilization of the physical laws. A second order training algorithm with dynamic derivatives is also used to improve the training efficiency and accuracy. With these new features, this model can significantly outperform the previous approach in terms of accuracy and efficiency. The network is trained and tested using experimental data as well as analytical results. It is shown that this new model can compensate for time step variations and can predict the clutch torque accurately for a wide range of operating conditions.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

A Robust Pressure Controller for a Variable Speed AC Motor Pump: Application to Aircraft Hydraulic Power Packages

2018 ◽  
Author(s):  
Nils Trochelmann ◽  
Phillip Bischof Stump ◽  
Frank Thielecke ◽  
Dirk Metzler ◽  
Stefan Bassett
Keyword(s):  
Distribution Systems ◽  
Controller Design ◽  
Dynamic Performance ◽  
Operating Conditions ◽  
Gear Pump ◽  
Variable Speed ◽  
Hydraulic Power ◽  
Worst Case ◽  
Wide Range ◽  
Pressure Controller

Highly integrated electro-hydraulic power packages with electric motor-driven pumps (EMP) are a key technology for future aircraft with electric distribution systems. State of the art aircraft EMPs are robust but lack efficiency, availability, and have high noise emissions. Variable speed fixed displacement (VSFD-) EMPs, combining a permanent magnet synchronous motor and an internal gear pump, show promising properties regarding noise reduction and energy efficiency. Though, meeting the strict dynamic requirements is tough with this EMP-concept. Speed limitations and inertia impose strong restrictions on the achievable dynamic performance. Moreover, the requirements must be met under a wide range of operating conditions. For a prototype aircraft VSFD-EMP a robust pressure controller design is proposed in this paper. In a first step the operating conditions of the EMP are defined, analyzing environmental conditions and impacts of the interfacing aircraft systems. Nonlinear and linear control design models are developed and validated by measurements at an EMP test rig built for this project. A conventional cascade pressure control concept is selected. This is motivated by the demand for simple, reliable, and proven solutions in aerospace applications. A controller is designed by applying classical loop shaping techniques. Robust stability and performance of the system are investigated through a subsequent μ-analysis. Finally, the controller is tested under nominal and worst case conditions in nonlinear simulations.

scholarly journals Robust state feedback controller design of STATCOM using chaotic optimization algorithm

2010 ◽  
Vol 7 (2) ◽  
pp. 253-268 ◽  
Author(s):  
Amin Safari ◽  
Hossein Shayeghi ◽  
Ali Heidar
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
State Feedback ◽  
Controller Design ◽  
Low Frequency ◽  
Operating Conditions ◽  
Feedback Controller ◽  
Static Synchronous Compensator ◽  
State Feedback Controller ◽  
Wide Range

In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM) using Chaotic Optimization Algorithm (COA) is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.

Parameter Identification of LuGre Friction Model: Experimental Set-up Design and Measurement

2015 ◽  
Author(s):  
Yun-Hsiang Sun ◽  
Tao Chen ◽  
Cyrus Shafai
Keyword(s):  
High Performance ◽  
Controller Design ◽  
Measurement Procedure ◽  
Friction Model ◽  
Operating Conditions ◽  
Dynamic Friction ◽  
Friction Modeling ◽  
Friction Behavior ◽  
Wide Range ◽  
Set Up

This work proposes a simple but general experimental approach including the rig design and measurement procedure to carry out a wide range of experiments required for identifying parameters for LuGre dynamic friction model. The design choice is based on accuracy of the estimated friction and flexibility in terms of changing contact conditions. The experimental results allow a complete LuGre model, which facilitates, but not limited to, other advanced friction modeling and high performance controller design if needed. In addition, several well-known dynamic friction features (varying break-away force, friction lag and presliding) are successfully demonstrated by our rig, which indicates the adequacy of our approach for capturing highly sophisticated and dynamic friction behavior over a wide range of operating conditions. The proposed set-up and the produced experimental data are believed to greatly facilitate the development of advanced friction compensation and modeling in friction affected mechanisms.

Development of optimal diesel injection strategy based on the prediction of performance and emissions using deep neural network

2021 ◽  
pp. 146808742110577
Author(s):  
Erdoğan Güner ◽  
Aliriza Kaleli ◽  
Kadir Bakirci ◽  
Mehmet Akif Ceviz
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Operating Conditions ◽  
Exhaust Emission ◽  
Pilot Injection ◽  
Data Set ◽  
Injection Strategy ◽  
Wide Range ◽  
Performance And Emissions ◽  
Main Injection

This study aims to determine the optimal injection strategy by predicting the performance and exhaust emission parameters of a four-cylinder CRDI engine under several operating conditions. The experimental determination procedure is challenging and expensive calibration task since it requires a high number of tests. Many studies have focused on a limited level of parameters. In this study, design of experiments technique and deep neural network (DNN) modeling are used together. The experimental data set for the model is created using Taguchi L16 and L32 orthogonal arrays. The DNN model is developed to predict [Formula: see text], [Formula: see text], HC, and CO emissions with speed, torque, injection timings and fuel quantities of each injection called as pilot1, pilot2, main, and post. In this way, it has become possible to evaluate the effects of a larger number of operating parameters in a wide range than the literature. The developed DNN model predicts the [Formula: see text], [Formula: see text], HC, and CO with R2 0.939, 0.943, 0.963, and 0.966, respectively. Additionally, RMSE and MAE values for the model are between 0.024 and 0.048. The proposed method compared with the conventional look-up table method performs better in reducing the complexity and cost of experiments and exploration of the effects of injection parameters on engine emission and performance characteristics in a wide engine operating range. In conclusion, until 2300 rpm at specified torque (90 Nm), it is found that 70% of fuel quantity should inject in main injection to minimize [Formula: see text] and [Formula: see text] emissions. The post injection quantity should be increased by reducing the amount of main injection from this operating condition on. Furthermore, it is observed that the ratios of pilot injection durations do not change with increasing engine speed, but quantity of first pilot injection is more than that of second pilot injection.

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