Sequential virtual motion camouflage method for nonlinear constrained optimal trajectory control

    Authors

    Y. J. Xu;G. Basset

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Automatica

    Keywords

    Nonlinear constrained optimization; Bio-inspired control; Optimal; trajectory design; PSEUDOSPECTRAL METHOD; COSTATE ESTIMATION; OPTIMIZATION; ALGORITHM; ROBOT; CONVERGENCE; COLLOCATION; STATE; STABILITY; DESIGN; Automation & Control Systems; Engineering, Electrical & Electronic

    Abstract

    Nonlinear constrained optimal trajectory planning is a challenging and fundamental area of research. This paper proposes bio-inspired fast-time approaches for this type of problems based on the inspiration drawn from the natural phenomenon known as the motion camouflage. Two algorithms are proposed: the virtual motion camouflage (VMC) subspace method and the sequential VMC method. As a hybrid approach, the sequential VMC method works through a two-step structure in each iteration. First, the VMC subspace method will solve for an optimal solution over a selected subspace. Second, an algorithm consisting of a linear programming and a line search will vary the subspace so that the next VMC subspace result will be guaranteed not to be worse than that of the current step. The dimension and time complexities of the algorithms will be analyzed, and the optimality of the solution via the sequential VMC approach will be studied. Through the VMC approaches, the state and control variables in the kinematics or dynamics models of vehicles in the selected subspace can be represented by a single degree-of-freedom vector, called the path control parameter vector. The reduction in dimension and no involvement of equality constraints will in practice make the convergence faster and easier, and a much smaller computational cost is expected. Two simulation examples, the Breakwell problem and a minimum time robot obstacle avoidance problem with different numbers of obstacles, are used to demonstrate the capabilities of the algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

    Journal Title

    Automatica

    Volume

    48

    Issue/Number

    7

    Publication Date

    1-1-2012

    Document Type

    Article

    Language

    English

    First Page

    1273

    Last Page

    1285

    WOS Identifier

    WOS:000306615800006

    ISSN

    0005-1098

    Share

    COinS