Optimal redundancy control of robot manipulators
International Journal of Robotics Research
Task-priority based redundancy control of robot manipulators
International Journal of Robotics Research
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities
A Stable Neuro-Adaptive Controller for Rigid Robot Manipulators
Journal of Intelligent and Robotic Systems
Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Inverse Kinematic Solution based on Lyapunov Function for Redundant and non-Redundant Robots
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
A Neural Network Adaptive Controller for End-effector Tracking of Redundant Robot Manipulators
Journal of Intelligent and Robotic Systems
Compensation of velocity and/or acceleration joint saturation applied to redundant manipulator
Robotics and Autonomous Systems
Modeling and adaptive control of redundant robots
Mathematics and Computers in Simulation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Obstacle avoidance for kinematically redundant manipulators using a dual neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Robotics and Autonomous Systems
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The paper presents a new controller approach applied to redundant robot manipulators constrained by mobile obstacles. The proposed controller is constructed in task space by using optimization strategy, in order to achieve a good trajectory tracking of the end effector even if the obstacles are fixed or mobile. The criterion to be optimized is chosen as the sum of the joint displacements energy and the internal penalty functions that take into account the obstacle positions. Any knowledge on the dynamic model is needed, only its structure. All unknown functions in the robot dynamical model, written in extended Cartesian space, are carried out using multilayer perceptron (MLP) neural networks. The adaptation laws of the neural parameters are obtained via closed loop stability analysis of the system (Lyapunov approach). In order to evaluate the proposed controller performance a 3 DOF robot manipulator evolving in a vertical space constrained by a mobile obstacle is used. The obtained results show its effectiveness.