Unwanted vibrations stemming from the energy-optimized design of Delta robots pose a challenge in their operation, especially with respect to precise reference tracking. To improve tracking accuracy, this paper proposes an adaptive mismatch-compensated iterative learning controller based on input shaping techniques. We establish a dynamic model considering the electromechanical rigid-flexible coupling of the Delta robot, which integrates the permanent magnet synchronous motor. Using this model, we design an optimization-based input shaper, considering the natural frequency of the robot, which varies with the configuration. We proposed an iterative learning controller for the delta robot to improve tracking accuracy. Our iterative learning controller incorporates model mismatch where the mismatch approximated by a fuzzy logic structure. The convergence property of the proposed controller is proved using a Barrier Composite Energy Function, providing a guarantee that the tracking errors along the iteration axis converge to zero. Moreover, adaptive parameter update laws are designed to ensure convergence. Finally, we perform a series of high-fidelity simulations of the Delta robot using Simscape to demonstrate the effectiveness of the proposed control strategy.

翻译：Delta机器人能量优化设计所引发的非期望振动对其运行构成挑战，尤其在精确参考轨迹跟踪方面。为提高跟踪精度，本文提出一种基于输入整形技术的自适应失配补偿迭代学习控制器。我们建立了考虑Delta机器人机电刚柔耦合特性的动力学模型，该模型集成了永磁同步电机。基于此模型，我们设计了一种考虑机器人随构型变化的固有频率的优化输入整形器。针对Delta机器人提出了一种迭代学习控制器以提升跟踪精度。该迭代学习控制器引入了由模糊逻辑结构近似的模型失配项。通过构建障碍复合能量函数证明了所提控制器的收敛性，确保沿迭代轴的跟踪误差收敛至零。此外，设计了自适应参数更新律以保证收敛性。最后，我们利用Simscape对Delta机器人进行了一系列高保真仿真，验证了所提控制策略的有效性。