• DocumentCode
    631019
  • Title

    Optimal trajectory generation for nonlinear systems based on double generating functions

  • Author

    Zhiwei Hao ; Fujimoto, Kenji ; Hayakawa, Yoshikazu

  • Author_Institution
    Dept. of Mech. Sci. & Eng., Nagoya Univ., Nagoya, Japan
  • fYear
    2013
  • fDate
    17-19 June 2013
  • Firstpage
    6382
  • Lastpage
    6387
  • Abstract
    A method based on the generating function for the finite time optimal control problems was proposed recently. A single generating function can generate a family of optimal inputs which are functions of the state for different boundary conditions. Therefore, a family of optimal trajectories for different boundary conditions can be obtained by numerical integration along the system dynamic equation. This paper proposes a method to compute a family of optimal trajectories for a nonlinear optimal control problem on a finite time interval by using a pair of generating functions. The proposed method reduces the online computational effort in calculating the numerical integration required in the method using a single generating function. It is useful to on-line nonlinear trajectory generation problems such as model predictive control. A numerical example illustrates the effectiveness of the proposed method.
  • Keywords
    integration; nonlinear control systems; optimal control; predictive control; trajectory control; boundary conditions; double generating functions; finite time interval; finite time optimal control problems; model predictive control; nonlinear optimal control problem; nonlinear systems; numerical integration; online computational effort; online nonlinear trajectory generation; optimal inputs; optimal trajectory generation; single generating function; system dynamic equation; Boundary conditions; Cost function; Equations; Mathematical model; Optimal control; Taylor series; Trajectory;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference (ACC), 2013
  • Conference_Location
    Washington, DC
  • ISSN
    0743-1619
  • Print_ISBN
    978-1-4799-0177-7
  • Type

    conf

  • DOI
    10.1109/ACC.2013.6580839
  • Filename
    6580839