In this paper, a hybrid neural fuzzy control scheme is proposed for the control of flexible-joint robot manipulators with unknown dynamics. The control strategy is based on a feed-forward artificial neural network to partially approximate the manipulator's inverse dynamics. A fuzzy sliding mode feedback controller is also used for the online adaptation of the neural network-based controller. Simulation results of various scenarios highlight the performance and stability of the proposed controller in compensating for the highly nonlinear unknown dynamics of the manipulator under different dynamical conditions and external disturbances
This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for trajectory control...
[[abstract]]In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode contr...
This paper presents a new control scheme utilizing fuzzy neural networks for trajectory control of r...
This paper presents an approach of cooperative control that is based on the concept of combining neu...
An adaptive control strategy has been developed for flexible-joint robotic manipulators in the prese...
A fuzzy sliding mode controller based on radial basis function neural network (RBFNN) is proposed in...
This paper proposes a position control strategy based on Artificial Neural Networks (ANN) in the fac...
In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical ...
The raised complicatedness of the dynamics of a robot manipulator considering joint elasticity makes...
The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for rob...
This paper presents a stable neuro-fuzzy (NF) adaptive control approach for the trajectory tracking ...
This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joi...
This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched ...
Industrial arms should be able to perform their duties in environments where unpredictable condition...
The learning space for executing general motions of a flexible joint manipulator is quite large and ...
This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for trajectory control...
[[abstract]]In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode contr...
This paper presents a new control scheme utilizing fuzzy neural networks for trajectory control of r...
This paper presents an approach of cooperative control that is based on the concept of combining neu...
An adaptive control strategy has been developed for flexible-joint robotic manipulators in the prese...
A fuzzy sliding mode controller based on radial basis function neural network (RBFNN) is proposed in...
This paper proposes a position control strategy based on Artificial Neural Networks (ANN) in the fac...
In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical ...
The raised complicatedness of the dynamics of a robot manipulator considering joint elasticity makes...
The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for rob...
This paper presents a stable neuro-fuzzy (NF) adaptive control approach for the trajectory tracking ...
This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joi...
This article addresses the control problem of robots with unknown dynamics and arbitrarily-switched ...
Industrial arms should be able to perform their duties in environments where unpredictable condition...
The learning space for executing general motions of a flexible joint manipulator is quite large and ...
This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for trajectory control...
[[abstract]]In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode contr...
This paper presents a new control scheme utilizing fuzzy neural networks for trajectory control of r...