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A Neural Network NARMA-L2 Tracking Control for Electronic Throttle System
Technical Paper
2022-01-0411
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In this paper, the Artificial Neural Network (ANN) control strategy based on the Nonlinear Auto Regressive Moving Average-Level 2 (NARMA-L2) technique has been used for tracking control of an electronic throttle body. The NARMA-L2 nonlinear plant model is first identified offline by training using a set of input-output data pairs measured at different operating conditions. This data was collected from an actual operation of the throttle body running in a closed-loop control system on a prototype vehicle. The identified NARMA-L2 plant model was then inverted and used to force the throttle output position to approximately track any reference inputs with multiple set-point changes at different operating conditions. The NARMA-L2 model was reconfigured to be an equivalent model of a feed-forward controller that can cancel not only the actual dynamic behavior of the throttle body but also the nonlinearity effects. This type of controller has great potential to overcome the difficulty of developing an inverse dynamic model of the throttle body [1] and has the ability to compensate any un-modeled nonlinear dynamic changes that may be excited due to environmental changes. These changes can include load, temperature, humidity, and external disturbances with noise effects that may be introduced into the closed-loop control of the throttle body during real time operation in the vehicle. The resulting closed loop control system used the NARMA-L2 architecture that have a multilayer perceptron neural network structure with the dynamic back-propagation algorithm becomes an implicit algebraic model that perfectly tracks any multiple set-point changes at different operating conditions. Testing results from real time implementation are provided to illustrate the new controller performance for tracking controls during actual operation on a prototype vehicle. The identified NARMA-L2 neuro-controller shows more robustness and better performs as compared to other controllers developed in previous work [1-3].
Authors
Citation
Al-Assadi, S., "A Neural Network NARMA-L2 Tracking Control for Electronic Throttle System," SAE Technical Paper 2022-01-0411, 2022, https://doi.org/10.4271/2022-01-0411.Also In
References
- Al-Assadi , S. A Neural Network-Based Direct Inverse Model Application to Adaptive Tracking Control of Electronic Throttle Systems SAE Technical Paper 01-0197 2014
- Al-Assadi , S. Neural Network-Based Model Reference Adaptive Control for Electronic Throttle Systems SAE Technical Paper 01-1628 SAE World Congress 2007 116 657 664
- Al-Assadi , S. , Breitinger , J. and Murphy , N. Tuning An Electronic Throttle Controllers using Computer-Aided Calibration Methodology SAE Technical Paper 01-0307 2006 115 126 133
- Al-Assadi , S. , Breitinger , J. and Murphy , N. Model-Based Friction and Limp-Home Compensation in Electronic Throttle Control Systems SAE Technical Paper 01-0857 2006
- She , Y. and Ye Huawen , Y. Robust Tracking Control of Automotive Throttle System without Current Sensor International Journal of Powertrain 2013 2 1 78 100
- Norgard , M. , Ravn , O. , Poulsen , N.K. , and Hansen , L.K. Neural Networks for Modelling and Control of Dynamic Systems London Springer Verlag 2000
- MathWorks Neural Network Toolbox- Version 4.0.6 (R14) 2005
- Narendra , K.S. and Mukhopadhyay , S. Adaptive Control using Neural Networks and Approximate Models IEEE Trans. Neural Networks 8 1997 475 485
- Hagan , M.T. , De Jesus , O. , and Schultz , R. Training Recurrent Networks for Filtering and Control Recurrent Neural Networks: Design and Applications Medsker , L. and Jain , L.C. CRC, Press 1999 311 340
- Moré , J.J. The Levenberg-Marquardt Algorithm: Implementation and Theory Numerical Analysis Watson , G.A. Springer Verlag 1977 105 116
- Hagan , M.T. and Menhaj , M. Training Feedforward Networks with Marquardt Algorithm IEEE Transactions on Neural Networks 5 6 1994 989 993
- Pukrittayakame , A. , De Jesus , O. , and Hagan , M.T. Smoothing the Control Action for NARMA-L2 Controllers Midwest Symposium on Circuits and System 3 2002 37 40