This article presents a novel adaptive composite learning (ACL) control strategy combining reinforcement learning and a disturbance observer (DOB) to address vibration issues in a flexible two‐link manipulator (FTLM) system affected by unknown spatiotemporally varying disturbances. Based on the assumed mode method, the FTLM system is initially transformed into an ordinary differential equation model, while effectively capturing the elastic deformation and vibration characteristics of the flexible link. A composite learning controller, based on the actor‐critic algorithm and DOB, is then developed to achieve trajectory tracking and vibration suppression in the FTLM system. The DOB in the controller compensates for unknown disturbances resulting in reduced system error. It is noting that the proposed optimal control strategy is continuously gathering system experience and evaluating the current policy's effectiveness. The stability and robustness of the closed‐loop system incorporating the composite controller are analyzed using Lyapunov's direct method, and the semi‐global uniform ultimate boundedness of the tracking and vibration errors are also demonstrated. To validate the effectiveness and superiority of the proposed ACL controller, comparative simulations and experiments are conducted on the Quanser experimental platform.

中文翻译：

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未知时空变化扰动柔性双连杆机械臂的自适应复合学习控制

本文提出了一种新颖的自适应复合学习（ACL）控制策略，将强化学习和扰动观察器（DOB）相结合，以解决受未知时空变化扰动影响的柔性双连杆机械臂（FTLM）系统中的振动问题。基于假设模态方法，将FTLM系统初步转化为常微分方程模型，同时有效地捕捉柔性连杆的弹性变形和振动特性。然后开发了一种基于 actor-critic 算法和 DOB 的复合学习控制器，以在 FTLM 系统中实现轨迹跟踪和振动抑制。控制器中的 DOB 可补偿未知干扰，从而减少系统误差。值得注意的是，所提出的最优控制策略正在不断收集系统经验并评估当前策略的有效性。采用李亚普诺夫直接法分析了复合控制器闭环系统的稳定性和鲁棒性，并证明了跟踪误差和振动误差的半全局一致极限有界性。为了验证所提出的ACL控制器的有效性和优越性，在Quanser实验平台上进行了对比仿真和实验。