Nonlinear Autoregressive Moving Average-L2 Based Model Reference Adaptive Control of Nonlinear Arm Nerve Simulator System

Abstract

This thesis considers the problem of using approximate methods for realizing the neural controllers for nonlinear SISO systems. In this thesis, we introduce the nonlinear autoregressive-moving average (NARMA-L2) model which are approximations to the NARMA model. The nonlinear autoregressive-moving average (NARMA-L2) model is an exact representation of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it is not convenient for purposes of neural networks due to its nonlinear dependence on the control input. In this thesis, nerves system based arm position sensor device is used to measure the exact arm position for nerve patients using the proposed systems. In this thesis, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite often, approximate methods are used for realizing the neural controllers to overcome computational complexity. Comparison have been made between the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model based model reference adaptive control for the desired input arm position (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMAL2 model based model reference adaptive control system.