A Neural Network NARMA-L2 Tracking Control for Electronic Throttle System*

Abstract

<div class="section abstract"><div class="htmlview paragraph">In this paper, the Artificial Neural Network (ANN) control strategy based on the Nonlinear Auto Regressive Moving Average-Level 2 (<i>NARMA-L2</i>) technique has been used for tracking control of an electronic throttle body. The <i>NARMA-L2</i> 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 <i>NARMA-L2</i> 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 <i>NARMA-L2</i> 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 [<span class="xref">1</span>] 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 <i>NARMA-L2</i> 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 <i>NARMA-L2</i> neuro-controller shows more robustness and better performs as compared to other controllers developed in previous work [<span class="xref">1</span>-<span class="xref">3</span>].</div><div class="htmlview paragraph">* This work was done while the author was at IAV Automotive Engineering Inc, 15620 Technology Drive, Northville, MI 48168.</div><div class="htmlview paragraph">† Currently working with MAHLE Powertrain LLC, 14900 GalleonCourt, Plymouth, MI 48170.</div></div>