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Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment

机译：与环境互动的分散式机器人轨迹跟踪控制

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Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness.
机译：尽管问题具有经典性质，但是对于软机器人（即在设计中故意引入柔顺性元素的机器人）的轨迹跟踪仍然提出了一些挑战。其中之一是设计一种控制器，该控制器可以在保持软机器人固有的物理特性的同时获得足够高的性能。确实，使用高增益反馈动作的经典控制方案从根本上改变了软机器人的自然柔顺性，从而有效地使它们变得僵硬，从而实际上破坏了其主要设计目的。作为一种替代方法，我们在这里考虑使用低增益反馈，同时利用前馈组件。为了应对动力学的复杂性和不确定性，我们采用了分散的，迭代学习的前馈作用，并结合了局部最优反馈控制。反馈和前馈控制操作的相对权限会根据学习到的组件的不确定程度进行调整。该方法的有效性已在几种机器人结构和工作条件上进行了实验验证，包括与环境的意外相互作用，其中保持柔软性对于安全性和坚固性至关重要。

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