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.

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.

Hybrid Neural Network for Modeling Automotive Clutches

1999 ◽  
Author(s):  
V. Parvataneni ◽  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. Tobler
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Hybrid Network ◽  
Hybrid Neural Network ◽  
Physical Knowledge ◽  
The Neural Network ◽  
Clutch Engagement ◽  
Wet Clutch ◽  
Engagement Process ◽  
Artificial Neural Network Ann

Abstract In this paper, artificial neural network (ANN) based models to capture the dynamic engagement torque of a wet clutch system are developed and analyzed. A two-layer recurrent ANN with output feedback is chosen as the baseline architecture since its simplicity allows easy implementation and efficient execution. Although this model exhibits good performance in capturing the overall mean level of the engagement torque as a function of time, it is unable to predict some of the important clutch dynamics behaviors. To improve the performance, additional neurons that represent the first principles of the clutch engagement process are incorporated into the neural network model. In other words, a hybrid model in which physical knowledge is implicitly embedded within the ANN architecture is derived. This hybrid model is trained and tested with experimental data. The results show that the performance of the hybrid network is much superior to that of the baseline ANN. It can successfully capture not only the trends, but also the detailed characteristics of the clutch engagement torque as a function of time.

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.

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

Neural Networks L2-Gain Controller Design for Nonlinear System

2011 ◽  
Vol 467-469 ◽  
pp. 1505-1510
Author(s):  
Dan Liu ◽  
Ni Hong Wang ◽  
Gui Ying Li
Keyword(s):  
Neural Network ◽  
Disturbance Rejection ◽  
Differential Inequality ◽  
Controller Design ◽  
Design Method ◽  
General Structure ◽  
The Neural Network ◽  
Network Weight ◽  
Finite Gain ◽  
L2 Gain

This paper proposes a new method that it uses the neural network to construct the solution of the Hamiltion-Jacobi inequality (HJ), and it carries on the optimization of the neural network weight using the genetic algorithm. This method causes the Lyapunov function to satisfy the HJ, avoides solving the HJ parital differential inequality, and overcomes the difficulty which the HJ parital differential inequality analysis. Beside this, it proposes a design method of a nonlinear state feedback L2-gain disturbance rejection controller based on HJ, and introduces general structure of L2-gain disturbance rejection controller in the form of neural network. The simulation demonstrates the design of controller is feasible and the closed-loop system ensures a finite gain between the disturbance and the output.

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