## Abstract

The feasibility of applying the stochastic parameter tuning method to the design of space vehicle flight control systems is studied. Stochastic parameter tuning is a form of optimization by which the probability of the flight control system's total mission achievement is maximized. Mission achievement probability is estimated by applying the Monte Carlo method to the results of a large number of simulated flights. The flight simulation models contain various types of uncertain parameters, the stochastic properties of which are defined a priori. The flight control system requirements are defined based on the results of the flight simulations, and an optimization algorithm called the mean tracking technique is used to tune the feedback/feedforward gains and other adjustable parameters of the flight control laws to maximize the probability of satisfying the requirements. The feasibility of the stochastic parameter tuning method is demonstrated by applying it to the design of the flight control system of a reentry space vehicle, a low-speed subscaled model of which was flight tested in 1996. The stochastic parameter tuning method improves the robustness of the flight control system. Although stochastic parameter tuning requires large computational resources, the recent advent of low-cost, high-performance computers means that it has become feasible and practical. Furthermore, distributed computation can allow a large number of flight simulations to be conducted within limited time and cost constraints. An asynchronous parallel computation using distributed low-cost computers is applied to the Monte Carlo flight simulation.

Original language | English |
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Pages (from-to) | 597-604 |

Number of pages | 8 |

Journal | Journal of Guidance, Control, and Dynamics |

Volume | 24 |

Issue number | 3 |

DOIs | |

Publication status | Published - 2001 |

## All Science Journal Classification (ASJC) codes

- Control and Systems Engineering
- Aerospace Engineering
- Space and Planetary Science
- Electrical and Electronic Engineering
- Applied Mathematics