Abstract — 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 ...
[[abstract]]This paper presents an adaptive neural net controller for controlling given plants which...
A study regarding the swing-up and stabilization problem of a double pendulum on a cart is presented...
The development of computational power is constantly on the rise and makes for new possibilities in ...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
Abstrac t- Neural networks can be used to identifY and control nonlinear mechanical systems. The obj...
In this paper, a motion and balance control scheme is introduced for inverted pendulums using artifi...
An adaptive neural network backstepping control for a class of uncertain nonlinear systems is presen...
AbstractAlthough the neural inverse model controllers have demonstrated high potential in the non-co...
Online trained neural networks have become popular in recent years in the design of robust and adapt...
The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by...
Using neural networks, this paper proposes a new model-following adaptive control design technique f...
The extreme nonlinearity of robotic systems renders the control design step harder. The consideratio...
A new model-following adaptive control design technique for a class of non-affine and non-square non...
A new model-following adaptive control design technique for a class of non-affine and non-square non...
This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. ...
[[abstract]]This paper presents an adaptive neural net controller for controlling given plants which...
A study regarding the swing-up and stabilization problem of a double pendulum on a cart is presented...
The development of computational power is constantly on the rise and makes for new possibilities in ...
In this paper the authors present two approaches for the control of an inverted pendulum on a cart. ...
Abstrac t- Neural networks can be used to identifY and control nonlinear mechanical systems. The obj...
In this paper, a motion and balance control scheme is introduced for inverted pendulums using artifi...
An adaptive neural network backstepping control for a class of uncertain nonlinear systems is presen...
AbstractAlthough the neural inverse model controllers have demonstrated high potential in the non-co...
Online trained neural networks have become popular in recent years in the design of robust and adapt...
The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by...
Using neural networks, this paper proposes a new model-following adaptive control design technique f...
The extreme nonlinearity of robotic systems renders the control design step harder. The consideratio...
A new model-following adaptive control design technique for a class of non-affine and non-square non...
A new model-following adaptive control design technique for a class of non-affine and non-square non...
This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. ...
[[abstract]]This paper presents an adaptive neural net controller for controlling given plants which...
A study regarding the swing-up and stabilization problem of a double pendulum on a cart is presented...
The development of computational power is constantly on the rise and makes for new possibilities in ...