Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.

翻译：变刚度执行器（VSA）的设计形式多样。针对此类非线性系统的传统模型依赖型控制方法存在开发成本高、设计假设依赖性强等问题。相比之下，机器学习通过基于实测数据训练模型并固有地考虑非线性特性，展现出极具潜力的替代方案。本研究提出一种通用的、基于学习的软体执行器位置与刚度控制方法。在引入软体气动变刚度执行器后，利用输入输出数据对模型进行学习。为此搭建了可自动测量关节可变刚度的实验台架。控制过程中，采用高斯过程预测达到期望位置与刚度所需的压力值。前馈误差平均为总压力范围的11.5%，并通过反馈控制进行补偿。软体执行器实验表明，该基于学习的方法无需模型知识即可实现位置与刚度的连续调节。