Robust convergence of learning update in task-dependent feedforward control

TitleRobust convergence of learning update in task-dependent feedforward control
Publication TypeConference Paper
Year of Publication1997
AuthorsGorinevsky, D., and G. Vukovich
Conference NameDecision and Control, 1997., Proceedings of the 36th IEEE Conference on
Pagination4708 -4713 vol.5
Date Publisheddec.
Keywordsattitude control, convergence, convergence of numerical methods, error compensation, feedforward, feedforward control, feedforward neural nets, function approximation, Jacobian matrices, Jacobian matrix, learning (artificial intelligence), learning control, neurocontrollers, online compensation, radial basis function network, RBF neural network, space vehicles, spacecraft control, task-level 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


a place of mind, The University of British Columbia

Electrical and Computer Engineering
2332 Main Mall
Vancouver, BC Canada V6T 1Z4
Tel +1.604.822.2872
Fax +1.604.822.5949

Emergency Procedures | Accessibility | Contact UBC | © Copyright 2021 The University of British Columbia