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Ant Colony Learning Algorithm for Optimal Control

  • Jelmer Marinus van Ast
  • Robert Babuška
  • Bart De Schutter
Part of the Studies in Computational Intelligence book series (SCI, volume 281)

Abstract

Ant colony optimization (ACO) is an optimization heuristic for solving combinatorial optimization problems and is inspired by the swarming behavior of foraging ants. ACO has been successfully applied in various domains, such as routing and scheduling. In particular, the agents, called ants here, are very efficient at sampling the problem space and quickly finding good solutions. Motivated by the advantages of ACO in combinatorial optimization, we develop a novel framework for finding optimal control policies that we call Ant Colony Learning (ACL). In ACL, the ants all work together to collectively learn optimal control policies for any given control problem for a system with nonlinear dynamics. In this chapter, we discuss the ACL framework and its implementation with crisp and fuzzy partitioning of the state space. We demonstrate the use of both versions in the control problem of two-dimensional navigation in an environment with variable damping and discuss their performance.

Keywords

Membership Function Optimal Control Problem Pheromone Trail Terminal Vertex Pheromone Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jelmer Marinus van Ast
    • 1
  • Robert Babuška
    • 1
  • Bart De Schutter
    • 2
  1. 1.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands
  2. 2.Delft Center for Systems and Control & Marine and Transport TechnologyDelft University of TechnologyDelftThe Netherlands

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