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標題: | 先進型連續螞蟻群聚最佳化多目標模糊控制及智慧型機器人沿牆走應用 Advanced Continuous Ant Colony Optimization for Multi-Objective Fuzzy Control with Intelligent Robot Wall-Following Applications |

作者: | 許嘉宏 Hsu, Chia-Hung |

關鍵字: | 先進型連續螞蟻群聚最佳化;advanced continuous ant colony optimization;模糊系統;多目標進化型演算法;智慧型機器人沿牆走應用;fuzzy system;multi-objective evolutionary algorithm;intelligent robot wall-following application |

出版社: | 電機工程學系所 |

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摘要: | 本篇論文提出三種使用不同連續型螞蟻群聚最佳化(CACO)演算法的進化型模糊系統，以避免耗時的專家設計規則法與全面的監督式輸入輸出訓練資料之收集。在提出的CACO演算法中，解的描述和產生是透過包含節點和路徑的圖形來說明。本篇論文第一個提出的演算法為先進型CACO (ACACO)演算法，它使用新穎的螞蟻路徑挑選方案作為新解產生和提高學習性能。ACACO被應用在多機電力系統中彈性交流輸電系統(FACTS)裝置的多目標模糊領先落後控制器設計；其中，多目標函數是透過線性化組合轉成單目標函數來求解。第二個提出的是物種差異進化(SDE)活化CACO (SDE-CACO)演算法，此法將物種差異進化突變運算引入CACO演算法裡，以便改善其性能。論文並將基於SDE-CACO之多目標第二類型模糊控制法應用在實際機器人執行沿牆走任務。論文提出了兩階段學習控制架構以解決多目標機器人控制問題。第三個提出的是多目標規則編碼先進型CACO (MO-RACACO)演算法。不同於上面兩種改良式CACO演算法只找出多目標最佳化問題中的單一解，MO-RACACO可找出Pareto最佳解集合。MO-RACACO同樣地被應用在解決包含多個控制目標的實際機器人沿牆走控制問題上。論文並透過和各式基於群體最佳化演算法之比較來驗證這三種多目標CACO最佳化演算法的性能。 This dissertation proposes three evolutionary fuzzy systems using different continuous ant colony optimization (CACO) algorithms, which avoids the time-consuming task of rule design by human experts and exhaustive collection of supervised input-output training pairs. Representations and generations of solutions in the proposed CACO algorithms are graphically explained in terms of nodes and path segments. The first algorithm is advanced CACO (ACACO), which uses a novel ant-path selection scheme for new solution generation and learning performance improvement. The ACACO is applied to multi-objective fuzzy lead-lag control of flexible AC transmission system (FACTS) devices in a multi-machine power system (PS), where multi-objective functions are linearly combined into a single objective function. The second one is a Species-Differential-Evolution (SDE) activated CACO (SDE-CACO) algorithm, which introduces a SDE mutation operation into a CACO algorithm for optimization performance improvement. The SDE-CACO is applied to multi-objective type-2 fuzzy control of a real robot performing a wall-following task. A two-stage learning control configuration is proposed to address the multi-objective robot control problem. The third one is a Multi-Objective Rule-Coded Advanced CACO (MO-RACACO) algorithm. Unlike the above two modified CACO algorithms that find a single solution in a multi-objective optimization problem, the MO-RACACO finds Pareto-optimal solutions. The MO-RACACO is also applied to the real robot wall-following control problem with multiple control objectives. Performances of these three optimization algorithms are verified through comparisons with various population-based optimization algorithms. |

URI: | http://hdl.handle.net/11455/9152 |

其他識別: | U0005-2911201205094900 |

Appears in Collections: | 電機工程學系所 |

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