Adaptive control for non-linear systems using artificial neural network and its application applied on inverted pendulum

This research work presents supervised Artificial Intelligence based control technique for an inverted pendulum. The inverted pendulum system is a classic control problem that is used in research. It is a suitable process to test prototype controllers due to its high non-linearities and lack of stability. Most traditional controllers (feedback linearisation, rule based control) are based around an operating points. This means that the controller can operate correctly if the plant/process operates around a certain point. These controllers fail if there is any sort of uncertainty or change in the unknown plant. Hence a neural network based supervised controller is designed and tested for inverted pendulum. Moreover (ADALINE) Adaptive linear element and (RBF) Radial basis Function based neural network controller do not require mathematical modeling of the system and they are capable of identifying complex nonlinear system. The main task is to design a controller which keeps the pendulum system stable. The Neural Network base supervised control technique reduces error efficiently. In this research work Adaptive neural toolbox is used, using ADALINE and RBF as ANN controller and the comparison between the ADALINE and RBF neural network is discussed. A comprehensive comparative study of performances of ADALINE and RBF is presented. ADALINE based control has given better performance.