The existing model-based control strategies for tendon-driven continuum soft robots neglect the dynamics of the actuation system. Nevertheless, such dynamics have an important impact on the closed-loop performance. This work analyzes the influence of the actuation dynamics in tendon-driven continuum soft robots performing trajectory-tracking tasks. To this end, we use singular perturbation (SP) theory to design controllers that account for such dynamics. We provide the analytical formulation of the SP controllers and their in-depth experimental validation. Additionally, we use high-and low-stiffness tendons to experimentally compare the performance of the proposed SP controllers against traditional feedback control schemes that disregard the actuation dynamics. The experimental results show that SP controllers outperform the approaches that neglect the actuation dynamics by reducing oscillations and achieving lower errors without relying on high gains. Furthermore, it is shown that neglecting the actuation dynamics may lead to instability when the tendons have a low stiffness coefficient.

The compliant nature of soft robots is appealing to a wide range of applications. However, this compliant property also poses several control challenges, e.g., how to deal with infinite degrees of freedom and highly nonlinear behaviors. This paper proposes a hybrid controller for a pneumatic-actuated soft robot. To this end, a model-based feedforward controller is designed and combined with a correction torque calculated via Gaussian process regression. Then, the proposed model-based and hybrid controllers are experimentally validated, and a detailed comparison between controllers is presented. Notably, the experimental results highlight the potential benefits of adding a learning approach to a model-based controller to enhance the closed-loop performance while reducing the computational load exhibited by purely learning strategies.

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics-informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model-based controllers originally developed with first-principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real-world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

Soft robots are intrinsically underactuated mechanical systems that operate under uncertainties and disturbances. In these conditions, this letter proposes two versions of PID-like control laws with a saturated integral action for the particularly challenging shape regulation task. The closed-loop system is asymptotically stabilized and matched constant disturbances are rejected using a very reduced amount of system information for control implementation. Stability is assessed on the underactuated dynamic model through the Invariant Set Theorem for two relevant classes of soft robots, i.e., elastically decoupled and elastically dominated soft robots. Extensive simulation results validate the proposed controllers.