Robust convergence of learning update in task-dependent feedforward control

This paper proposes and studies an algorithm for task-level control based on a radial basis function network approximation of the optimal task input vector on parameters of the task. A learning update scheme is proposed for online compensation for the inaccuracy of the model used in the controller design. The update approximates the Jacobian of the task input-output mapping using an off-line design model. Deadzone convergence of this learning scheme in the presence of modeling errors is proved and constructive estimates of the convergence robustness parameters are obtained