## Abstract

An optimal control problem can be formulated through a set of differential equations describing the trajectory of the control variables that minimize the cost functional (related to both state and control variables). Direct solution methods for optimal control problems treat them from the perspective of global optimization: i.e. perform a global search for the control function that optimizes the required objective. In this article we use a recently developed ecologically inspired optimization technique called Invasive Weed Optimization (IWO) for solving such optimal control problems. Usually the direct solution method operates on discrete n-dimensional vectors and not on continuous functions. Consequently it can become computationally expensive for large values of n. Thus, a parameterization technique is required to represent the control functions using a small number of real-valued parameters. Typically, direct methods based on evolutionary computing techniques parameterize control functions with a piecewise constant approximation. This has obvious limitations both for accuracy in representing arbitrary functions, and for optimization efficiency. In this paper a new parameterization is introduced using Bézier curves, which can accurately represent continuous control functions with only a few parameters. It is combined with IWO into a new evolutionary direct method for optimal control. The effectiveness of the new method is demonstrated by solving a wide variety of optimal control problems.

Original language | English (US) |
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Pages (from-to) | 1195-1203 |

Number of pages | 9 |

Journal | Engineering Applications of Artificial Intelligence |

Volume | 24 |

Issue number | 7 |

DOIs | |

State | Published - Oct 2011 |

Externally published | Yes |

## All Science Journal Classification (ASJC) codes

- Control and Systems Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering

## Keywords

- Control Vector Parameterization (CVP)
- Differential equations
- Genetic Algorithms
- Invasive Weed Optimization
- Metaheuristics
- Optimal control