Journal of Intelligent and Robotic Systems
Dynamic system identification via recurrent multilayer perceptrons
Information Sciences—Informatics and Computer Science: An International Journal
Systems Analysis Modelling Simulation
A real-time neuro-adaptive controller with guaranteed stability
Applied Soft Computing
An intelligent robust tracking control for electrically-driven robot systems
International Journal of Systems Science
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An Algebraic Approach for Accurate Motion Control of Humanoid Robot Joints
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Robust cascaded control of propeller thrust for AUVs
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Neural adaptive singularity-free control by backstepping for uncertain nonlinear systems
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Adaptive neural network control for strict-feedback nonlinear systems using backstepping design
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Information Sciences: an International Journal
Robust fin control for ship roll stabilization by using functional-link neural networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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A robust neural-network (NN) controller is proposed for the motion control of rigid-link electrically driven (RLED) robots. Two-layer NN's are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned online, with no off-line learning phase required. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of RLED robots without any modifications