隨著協作機器人與智慧製造技術的快速發展，機械手臂已逐漸由傳統隔離式工業環境，至現在可與人類共享工作空間之人機協作場域。有鑑於此，本論文提出一套結合動態軌跡生成、順應控制、賽局決策與安全控制之人機協作控制架構。在軌跡規劃方面，本論文採用動態運動原語作為參考軌跡生成方法，使系統具有示範軌跡重現、起終點重設與執行速度調整之能力。為提升人機互動下之柔順性，本論文結合導納控制，利用外力資訊產生位置與速度修正量，使機械手臂在執行任務的同時，仍能對人類施力作出柔順回應。於外層決策部分，本論文引入賽局理論，透過動態報酬矩陣與納許均衡分析最佳動作，決定機械手臂之互動策略；於內層控制部分，則透過基於 Riccati 方程式之合作追蹤控制器，權衡動態運動原語與導納控制兩類輸出，使控制結果同時兼顧任務追蹤性能與順應互動需求。為確保人機協作過程中之安全性，本論文導入控制障礙函數，並結合符號距離函數與二次規劃方法，建立安全控制架構。最後本論文將提出之方法進行整合，完成一套完整人機協作控制架構，可作為未來協作機器人應用於共享工作空間與服務型場域之控制基礎。

With the rapid development of collaborative robots and intelligent manufacturing technologies, robotic manipulators have gradually evolved from traditional isolated industrial environments to human-robot collaboration scenarios in which they share the workspace with humans. Consequently, this thesis proposes a human-robot collaboration control framework that integrates dynamic trajectory generation, compliant control, game-theoretic decision-making, and safety control.
For trajectory planning, Dynamic Movement Primitives (DMPs) are adopted as the reference trajectory generation method, enabling the system to reproduce demonstrated trajectories, reset start and goal positions, and adjust execution speed. To improve compliance during human-robot interaction, admittance control is employed to generate position and velocity command corrections based on external force information, allowing the manipulator to respond compliantly to human-applied forces while still performing the task. At the outer decision-making layer, game theory is introduced to determine the interaction strategy of the manipulator through a dynamic payoff matrix and Nash equilibrium analysis of the best action. At the inner control layer, a cooperative tracking controller based on the Riccati equation is employed to balance the outputs of DMPs and admittance control, so that the resulting control action can simultaneously satisfy task tracking performance and compliant interaction requirements. To ensure safety during human-robot collaboration, Control Barrier Functions are further incorporated, together with signed distance functions and quadratic programming, to establish a safety control framework. Finally, the proposed methods are integrated into a complete human-robot collaboration control architecture, which can serve as a control foundation for future collaborative robot applications in shared workspaces and service-oriented environments.

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