Generalized Adaptive Linear Element (GADALINE) Artificial Neural Network (ANN) as an Artificial Intelligence (AI) technique is used in this paper to online adaptive control of a Non-linear Inverted Pendulum (IP) system. The ANN controller is designed with specifications as: network type is three (Input, Hidden and Output) layered Feed-Forward Network (FFN), training is done by Widrow-Hoffs delta rule or Least Mean Square algorithm (LMS), that updates weight and bias states to minimize the error function. The research is focused on how to adapt the control actions to solve the problem of “parameter variations”. The method is applied to the Nonlinear IP model with the application of some uncertainties, and the experimental results show that the system responds very well to handle those uncertainties.

This paper considers the trajectory tracking control of linear inverted pendulum (IP) system. First the linearized model of IP is derived to facilitate the control design. To avoid non robust reaching phase, integral sliding mode control (ISMC) has been proposed but single variable case is tested. Linear IP is a multivariable system having angle of pendulum and position of cart are two variables to be controlled. In control design, the LQR control is designed as a nominal control to get the desired trajectory. Then discontinuous control using integral sliding mode(ISM) is introduced to get desired trajectory tracking in the presence of uncertainties. This control is robust to the model uncertainties and disturbances during entire motion of the states. The simulation results are presented to show the effectiveness of proposed control scheme. The results are compared with LQR control to show the integral sliding mode control is having better tracking performance in the presence of uncertainties.