Multilayer feedforward networks are universal approximators
Neural Networks
Neural Network Control of Coordinated Multiple Manipulator Systems
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Robotics and Autonomous Systems
IEEE Transactions on Robotics
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
A novel fused neural network controller for lateral control of autonomous vehicles
Applied Soft Computing
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In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.