Technical Paper
A Neural Network-Based Direct Inverse Model Application to Adaptive Tracking Control of Electronic Throttle Systems
2014-04-01
2014-01-0197
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.