Neural Network-Based Model Reference Adaptive Control for Electronic Throttle Systems

The purpose of this paper is to use a multilayer perceptron neural network model to identify and control a non-linear electronic throttle system. The neural network model, which represents the dynamic behaviour of the non-linear throttle servo system, was first identified at different operating conditions. The neural network controller model was then designed (or trained) with the throttle identifier network model, so that the tracking control position of the throttle system follows a reference model. The neural network controller training is computationally expensive and requires the use of the dynamic backpropagation algorithm, which is significantly time consuming during on-line implementation. For this reason, the throttle identifier network model is used to assist in training the neural controller in off-line mode. The neural network controller was trained with the same inputs that are fed to the actual throttle system to produce the same output. An adaptation algorithm was used to reduce the difference between the outputs by adjusting the weights and biases parameters of the controller network model. The tracking control performance of the throttle control system using the neural network controller was compared to the classical adaptive PID controller [1]. The simulation results reveal that, tracking control was achieved with the neural network controller, which satisfied all the requirements for the control performance.