Supervised Online Adaptive Control of Inverted Pendulum System Using ADALINE Artificial Neural Network with Varying System Parameters and External Disturbance
Автор: Sudeep Sharma, Vijay Kumar, Raj Kumar
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 8 vol.4, 2012 года.
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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.
Generalized Adaptive Linear Element (GADALINE) Artificial Intelligence (AI) Inverted pendulum (IP), Artificial Neural Network (ANN), Feed-Forword Network (FFN), Least Mean Square algorithm (LMS)
Короткий адрес: https://sciup.org/15010296
IDR: 15010296
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