A direct adaptive control scheme is developed using orthonormal activation function-based neural networks (OAFNN's) for trajectory tracking control of a class of nonlinear systems. Multiple OAFNN's are employed in these controllers for feedforward compensation of unknown system dynamics. Choice of multiple OAFNN's allows a reduction in overall network size reducing the computational requirements. The network weights are tuned on-line, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The developed neural controllers are evaluated experimentally and the experimental results are shown to support theoretical analysis. The effects of network parameters on system performance are experimentally evaluated and are presented in this research. The superior learning capability of OAFNN's is demonstrated through experimental results. The OAFNN's were able to model the true nature of the nonlinear system dynamics characteristics for a rolling-sliding contact as well as for stiction.

This paper details methods pertaining to the vision guided robotic control of fabric motion for performing simulated joining operations along the fabric edge. Robot motion paths are determined from visual information defining the position of the fabric edges. The function of the shape analysis and motion control algorithms is demonstrated via experimentation with an automated fabric handling system, Results of these tests show that using machine vision for planning robot motion provides an effective solution for implementing robotic fabric manipulation.

Robot hands capable of grasping a wide variety of objects at imprecisely known locations would increase the number of industrial robot applications in manufacturing. An important part of this problem is the ability to reposition objects that have been grasped in a mislocated position. A lumped-parameter model for a two degree-of-freedom robot hand is presented and several control strategies for repositioning objects are presented. Simulations of these control strategies indicate that using position control for one finger while simultaneously using force control for the other finger gives the best combination of fast motion, acceptable grasping forces, and small steady-state positioning errors.