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A Neural Network-Based Direct Inverse Model Application to Adaptive Tracking Control of Electronic Throttle Systems
Technical Paper
2014-01-0197
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper presents another application [1] of using Artificial Neural Networks (ANN) in adaptive tracking control of an electronic throttle system. The ANN learns to model the experimental direct inverse dynamic of the throttle servo system using a multilayer perceptron neural network structure with the dynamic back-propagation algorithm. An off-line training process was used based on an historical set of experimental measurements that covered all operating conditions. This provided sufficient information on the dynamics of the open-loop inverse nonlinear plant model. The identified ANN Direct Inverse Model (ANNDIM) was used as a feed-forward controller combined with an adaptive feed-back gains (PID) controller scheduled [2] at different operating conditions to provide the robustness in tracking control to un-modeled dynamics of the throttle servo system. The un-modeled dynamics are mainly related to the strong nonlinearity functions that may excite the system with external un-measurable disturbances and noise effects. The feed-forward ANNDIM is first used to emulate the inverse dynamics of the DC servo system. However, the variations in nonlinear dynamics of the throttle body during actual operation cause some error in the prediction of the exact inverse dynamic obtained from ANNDIM. Therefore, by adding the feed-back PID adaptive controller term in the control loop will compensate the model mismatches for these un-modeled dynamic variations and improve the overall control performance. Practical implementation results using rapid prototype real-time system testing are provided to illustrate the performance and effectiveness of the proposed method in tracking controls of multiple set-point changes at different operating conditions.
Citation
Al-Assadi, S., "A Neural Network-Based Direct Inverse Model Application to Adaptive Tracking Control of Electronic Throttle Systems," SAE Technical Paper 2014-01-0197, 2014, https://doi.org/10.4271/2014-01-0197.Also In
References
- Al-Assadi , S. Neural Network-Based Model Reference Adaptive Control for Electronic Throttle Systems 2007-01-1628 2007 10.4271/2007-01-1628
- Al-Assadi , S. , Breitinger , J. , and Murphy , N. Tuning An Electronic Throttle Controllers Using Computer-Aided Calibration Method 2006-01-0307 2006 10.4271/2006-01-0307
- She Yun and Ye Huawen Robust Tracking Control of Automotive Throttle System without Current Sensor International Journal of Powertrain 2 1 78 100 March 2013
- Norgard , M. , Ravn O. , Poulsen N.K. , and Hansen L.K. Neural Networks for Modelling and Control of Dynamic Systems Springer-Verlag London 2000
- Al-Assadi , S. , Breitinger , J. , and Murphy , N. Model-Based Friction and Limp Home Compensation In Electronic Throttle Control 2006-01-0857 2006 10.4271/2006-01-0857
- Al-Assadi , S. , Breitinger , J. , and Traver , M. Electronic Throttle Simulation Using Nonlinear Hammerstein Model 2006-01-0112 2006 10.4271/2006-01-0112
- MathWorks Neural Network Toolbox- Version 4.0.6 (R14) July 2005
- Al-Assadi , S. , Breitinger , J. , and Murphy , N. Engine Torque Mapping Using Computer-Aided Calibration 2005-01-0055 2005 10.4271/2005-01-0055