中文摘要

由於短距離的感知和通訊及有限的計算能力，單一代理者的應用範疇極為限制，反之， 一整隊的代理者可彼此互相合作，執行多範疇指定的自動化任務。整隊代理者的集體行為，例如，廣域的空間分佈、強健性、高可擴充性、低成本，將展現比單一代理者更具優勢的特性，因此，在負擔得起之成本考量及小尺寸的前提下，多代理者可完成單代理者無法達到的任務。
本論文聚焦於具有不確定性及輸入飽和的非線性動態多代理者之適應有限時間合作控制。為了達成有限時間的無合作誤差，所建議的適應控制具有分數指數之非線性濾波誤差、時變的切換增益、即時地學習動態不確定性的補償機制。本論文之領航者具有非線性動態並可追蹤所(即時)規劃之想要軌跡，而且至少一代理者必須與領航者進行溝通並且與鄰近之代理者達成所需的合作或隊形控制。本論文將設計如下三種適應控制器:(一)具有輸入飽和的模糊適應有限時間合作控制(FAFTCCIS)，(二)具有迴歸網路分數學習的有限時間隊形控制(RNNFL-FTFTCC)，(三)具線上迴歸網路基準的有限時間隊形控制(ORNN-FTFTC)。並將他們分別應用於(一)智慧廚師合作控制，(二)具隊形變化的多四旋翼之隊形控制，(三)具異質的六旋無人直升機及無人直升機之隊形控制。證明所設計控制器之有效性及強健性。

Abstract
Due to short sensing and communication capabilities, and constrained computational power, single agent has limited capabilities in vast application areas. In contrast, a team of agents coordinates with each other to carry out specific tasks without human intervention in many applications. Their collective behavior exhibits significant advantages compared to a single sophisticated agent, including large-scale spatial distribution, robustness, high scalability, and low cost. Thus, the utilization of large-scale multi-agent systems (MASs) with affordable costs and smaller sizes can attain tasks that are difficult to be accomplished by an individual agent.
The focus of this dissertation is on the design of adaptive finite-time cooperative control for nonlinear dynamic MASs with uncertainties and control input saturation. A fuzzy adaptive finite-time cooperative control with input saturation (FAFTCCIS), a recurrent neural network fractional learning for fixed-time formation tracking constrained control (RNNFL-FTFTCC), and an online recurrent neural network based finite-time formation tracking control (ORNN-FTFTC) are proposed. To accomplish the null cooperation error in finite time, the proposed adaptive controls possess the nonlinear filtering error with dynamic fraction order, a time-varying switching gain, and the on-line learning compensation mechanisms for the dynamic lumped uncertainties. In all of the works in this dissertation, the leader is a real dynamic system that tracks an assigned desired trajectory. Moreover, at least one agent must communicate with the leader and the information of neighborhood agents is required to accomplish the assigned task. Finally, the excellent simulation results for the cooperative control of intelligent chef, the formation of a group of quadrotors with formation change, and the formation of heterogeneous hexacopter unmanned aerial vehicles (HUAVs) and unmanned helicopters (UHs) using FAFTCCIS, RNNFL-FTFTCC and ORNN-FTFTC, respectively, are used to validate the effectiveness and robustness of the proposed control methods.

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