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ARTIFICIAL NEURAL NETWORK-BASED NONLINEAR DYNAMIC MODELLING OF THE TWIN-ROTOR MIMO SYSTEM

This paper investigates the development of an adaptive dynamic non linear model inversion control law for a twin rotor multi-input multi-output (MIMO) system TRMS utilizing artificial neural networks. The TRMS is an aerodynamic test rig representing the control challenging of modern air vehicles. A highly non linear one degree freedom model of the TRMS is considered in this paper and a non linear inverse model is developed for the pitch channel. In the absence of the model inversion errors, an artificial neural network (ANN) model in place of a proportional-integralderivative (PID) controller is used to enhance the tracking performance of the system. The neural network model is developed using backpropagation algorithm with Levenberg-Marquardt (LM) training method. The responses between the reference signals and empirical based models of the TRMS are used to validate the accuracy of the models. Simulation results under MATLAB Simulink show the improvement of response and superiority of simplified neural network controller.