A robust line search for learning control

In this paper a new line search for a Newton-Raphson learning control algorithm is presented. Theorems and rigorous proofs of its increased robustness over existing line searches are provided, and numerical examples are used to further validate the theorems.

[1]  Andrew A. Goldenberg,et al.  Learning approximation of feedforward dependence on the task parameters: Experiments in direct-drive manipulator tracking , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[2]  Dimitry Gorinevsky,et al.  An algorithm for on-line parametric nonlinear least square optimization , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[3]  Nader Sadegh,et al.  A robust line search for learning control , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[4]  N. Sadegh,et al.  Minimum time trajectory learning , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[5]  K. E. Avrachenkov Iterative learning control based on quasi-Newton methods , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[6]  Dimitry Gorinevsky An application of on-line parametric optimization to task-level learning control , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[7]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[8]  Danwei Wang,et al.  Learning control for a class of nonlinear differential-algebraic systems with application to constrained robots , 1996, J. Field Robotics.

[9]  Mo Jamshidi,et al.  Learning control of robot manipulators , 1992 .