Predictive reference shaping for constrained robotic systems using evolutionary algorithms

  • Authors:

  • Mario Luca Fravolini;Antonio Ficola;Michele La Cava

  • Affiliations:

  • Dipartimento di Ingegneria Elettronica e dell'Informazione, Universití di Perugia, Via G. Duranti No. 93, Perugia 06125, Italy;Dipartimento di Ingegneria Elettronica e dell'Informazione, Universití di Perugia, Via G. Duranti No. 93, Perugia 06125, Italy;Dipartimento di Ingegneria Elettronica e dell'Informazione, Universití di Perugia, Via G. Duranti No. 93, Perugia 06125, Italy

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper proposes a method for reducing the trajectory tracking errors of robotic systems in presence of input saturation and state constraints. Basing on a finite horizon prediction of the future evolution of the robot dynamics, the proposed device online preshapes the reference trajectory, minimizing a multi-objective cost function. The shaped reference is updated at discrete time intervals and is generated taking into account the full nonlinear robot dynamics, input and state constraints. A specialized Evolutionary Algorithm is employed as search tool for the online computation of a sub-optimal reference trajectory in the discretized space of the control alternatives. The effectiveness of the proposed method and the online computational burden are analyzed numerically in two significant robotic control problems; furthermore a comparison of the performance provided by this method and an iterative gradient-based algorithms are discussed.