E-051

Improving Trajectory Tracking by Feedforward Evolutionary Multivariable Constrained Optimization

Authors: Mario Luca Fravolini, Antonio Ficola, Michele La Cava
Affiliation: Dipartimento di Elettronica e dellInformazione - Università di Perugia

Abstract
Improvements in positioning accuracy and reduction of trajectory tracking error in robotic systems require advanced control laws; these should take into account also multivariable concurrent specifications and be able to handle inputs and state constraints. In this work these requirements are considered exploiting a feedforward model-based predictive controller in which the control law is planned on-line on the basis of a multi-objective cost function, the minimization of which is executed by means of an Evolutionary Algorithm. The proposed scheme has been applied to an existing robust feedback control scheme, to achieve a more accurate trajectory tracking of the tip of a flexible link. Real time execution is possible; moreover, notwithstanding the stochastic inference engine of the Evolutionary Algorithms, the proposed scheme is sufficiently reliable, since it reveals a high degree of repeatability of the control signals.

Antonio Ficola
Dipartimento di Ingegneria Elettronica e dell'Informazione
Via G. Duranti 93
06125 Perugia - Italy
Fax: +39 075 5852654
Tel: +39 075 5852679
ficola@diei.unipg.it