Soft robotics has grown exponentially during the past two decades due to the possibility of expanded manipulation capabilities over existing rigid robots in complex, unstructured environments. Additionally, soft robots can mitigate current safety risks associated with rigid robots due to their softness. The inspiration for soft robotics has been mainly due to the many examples from nature, such as the agile environmental interactions of the elephant trunk and octopus tentacles. Over the past two decades, several applications ranging from underwater operations to minimally invasive surgeries to space operations have been identified for soft robots. Motivated by these, the overall objective of this dissertation is to study and develop control frameworks for high-fidelity motion control of soft robotic systems. This entails exploiting generalized dynamics models for robust/adaptive control strategies for achieving various operational tasks involved in non-ideal environments, utilizing integrated sensing technologies, and investigating control of underactuated soft robotic systems.

This dissertation delve into passivity-based adaptive task space control for soft robots, mitigating uncertainty in the parameters as accurate parameter estimation is particularly hard in soft robotic systems. Further, this approach is extended to task space bilateral teleoperation of a soft follower-rigid leader system exploiting null space velocity tracking to achieve sub-task goals such as conforming to the degree of curvature limits in the soft robot. An enhanced dynamics model is also introduced tailored for planar soft robots and elaborate on passivity-based robust control methods for task space trajectory tracking within this context. This enhanced dynamics model is subsequently extended to encompass 3D spatial soft robots and a comprehensive framework for passivity-based robust task space bilateral teleoperation is discussed. Extensive numerical simulations and experiments are conducted to illustrate the efficacy of these proposed control frameworks. Moreover, to deploy soft robots in the real world, this dissertation study integrated sensing and control of soft robots and a stretchable soft-sensing skin for proprioception s introduced. The mapping from the strain signal to the curvature degree is estimated using a recurrent neural network. Further, an adaptive control framework for curvature tracking is proposed, leveraging the soft stretchable sensing skins and providing experimental evidence of its successful application.

This dissertation also introduces a novel robotic system known as the hybrid rigid-soft robot, composed of serially attached rigid and soft links, offering a fusion of the dexterity inherent to soft robots with the precision and payload capacity associated with rigid counterparts. Notably, the study demonstrates that well-established passivity-based adaptive and robust control techniques can effectively apply to this unique class of robots. A soft inverted pendulum with a revolute base is also introduced, establishing a scientific foundation and a methodological approach for introducing innovative soft robots in various practical applications. An energy-based controller is discussed for the swing-up and stabilization of the soft inverted pendulum system, highlighting the efficacy of the controller through simulations. Further, a comprehensive control architecture is developed for the swing-up and stabilization of a class of underactuated mechanical systems, including the soft inverted pendulum, by applying output partial feedback linearization and linear control techniques that avoid switching between controllers. The utility of this control architecture is illustrated using numerical simulations on the soft inverted pendulum.

These research endeavors collectively contribute to advancing the understanding of soft robotics and developing effective control strategies for various dexterous soft robotic systems.