Design Optimization and Data‐Driven Static Modeling for Active Fiber‐Reinforced Elastomeric Enclosures

Fiber‐reinforced elastomeric enclosures are widely used due to their lightweight, low‐cost, and compliant structure. However, these actuators produce only a single motion when pressurized, based on their fixed, embedded fibers. In contrast, active fiber‐reinforced elastomeric enclosures (AFREEs) use active fiber‐constraint layers to create different motions, enabling versatility. In this paper, we introduced and verified a static fiber model to optimize AFREEs. Next, we created AFREEs that use both embedded and active constraints and measured their twist. We implemented two machine learning (ML) models, a support vector machine (SVM) and a fully connected neural network (FCNN), to create a forward and inverse static response model. The static fiber model yielded an average error of 2.5%. Our AFREE twisted up to 110°. There were strong fits for the forward static response model (SVM: R 2 = 0.9917; FCNN: R 2 = 0.9901) and moderate fits for the inverse static response model (SVM: R 2 = 0.6985; FCNN: R 2 = 0.7656). Our results show the strength of data‐driven AFREE modeling, while laying the groundwork for customizable AFREE units.