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。针对这些非线性系统的传统基于模型的控制方法需要大量工程投入，且依赖于设计相关的假设条件。相比之下，机器学习作为一种极具前景的替代方案，其模型基于实测数据进行训练，能够天然地处理非线性特性。本研究提出了一种普适的基于学习的方法，用于软体执行器的位置与刚度控制。在介绍一种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，我们搭建了测试平台，可自动测量关节的可变刚度。在控制过程中，采用高斯过程预测实现目标位置与刚度所需的气压值。前馈控制误差平均为总气压范围的11.5%，并通过反馈控制进行补偿。针对该软体执行器的实验表明，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。