• DocumentCode
    2712796
  • Title

    Generalized Policy Iteration for continuous-time systems

  • Author

    Vrabie, Draguna ; Lewis, Frank L.

  • Author_Institution
    Autom. & Robot. Res. Inst., Univ. of Texas at Arlington, Fort Worth, TX, USA
  • fYear
    2009
  • fDate
    14-19 June 2009
  • Firstpage
    3224
  • Lastpage
    3231
  • Abstract
    In this paper we present a unified point of view over the approximate dynamic programming (ADP) algorithms which have been developed in the last years for continuous-time (CT) systems. We introduce here, in a continuous-time formulation, the generalized policy iteration (GPI), and show that in effect it represents a spectrum of algorithms which has at one end the exact policy iteration (PI) algorithm and at the other the value iteration (VI) algorithm. At the middle part of the spectrum we formulate for the first time the optimistic policy iteration (OPI) algorithm for CT systems. We introduce the GPI starting from a new formulation for the PI algorithm which involves an iterative process to solve for the value function at the policy evaluation step. The GPI algorithm is implemented on an actor/critic structure. The results allow implementation of a family of adaptive controllers which converge online to the solution of the optimal control problem, without knowing or identifying the internal dynamics of the system. Simulation results are provided to verify the convergence to the optimal control solution.
  • Keywords
    continuous time systems; dynamic programming; iterative methods; optimal control; actor/critic structure; adaptive controllers; approximate dynamic programming algorithms; continuous-time systems; generalized policy iteration; iterative process; optimal control problem; optimistic policy iteration algorithm; policy evaluation step; value iteration algorithm; Control systems; Dynamic programming; Iterative algorithms; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Optimal control; Programmable control; State feedback;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2009. IJCNN 2009. International Joint Conference on
  • Conference_Location
    Atlanta, GA
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-3548-7
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2009.5178964
  • Filename
    5178964