Iterative minimization of H2 control performance criteria

  • Authors:

  • Alexandre S. Bazanella;Michel Gevers;Ljubiša Mišković;Brian D. O. Anderson

  • Affiliations:

  • Electrical Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre-RS, Brazil and Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la- ...;Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;Department of Mathematical Engineering, Université Catholique de Louvain, Louvain-la-Neuve, Belgium;Research School of Information Science and Engineering, Australian National University, Canberra, Australia and National ICT Australia, Canberra, Australia

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2008

Quantified Score

Hi-index 22.15

Visualization

Abstract

Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H"2 performance criterion. Typically, batches of input-output data collected from the system are used to feed directly a gradient descent optimization - no process model is used. A limiting factor in the application of these methods is the lack of useful conditions guaranteeing convergence to the global minimum; several adaptive control algorithms suffer from the same limitation. In this paper the H"2 performance criterion is analyzed in order to characterize and enlarge the set of initial parameter values from which a gradient descent algorithm can converge to its global minimum.