Achieving high-fidelity haptic feedback in teleoperated systems is crucial, particularly in applications such as medical, industrial, military, space, underwater explorations, surgical training, and robot-assisted minimally invasive surgery (MIS). Appropriate design of haptic interfaces (master robot) and control algorithms enhance the quality of haptic feedback by addressing objectives such as stability and transparency. Accurate reproduction of the virtual/remote environment remains a challenge due to the intricacies of providing all necessary sensations simultaneously. In the first part of the work, a novel haptic device with three degrees of freedom is developed to render touch sensations such as stiffness, texture, shape, and shear concurrently. The developed haptic device is extended to a grasper that provides multi-contact sensations. The vibro-actuators in the device contribute to increasing the number of perceivable textures. In addition, a flexure-based backlash-free mechanism is utilized for the power transmission to improve the fidelity. The proposed device is characterized and validated through experiments.

In the second part of the work, a novel multimodal adaptive robust and RBFNN (Radial basis function-based neural network) based control algorithms for teleoperated systems with time-varying delays have been developed. In addition to the time delay, the most challenging issues that affect haptic feedback are model uncertainties, parametric variations, and external disturbances. The proposed methods ensure robustness against bounded perturbations and high-frequency unmodeled dynamics using a modified sliding-mode controller alongside the adaptive and neural network scheme. Here, parametric convergence is achieved swiftly with a milder condition than a stronger persistent excitation condition on the regressor signal, resulting in the enhancement of transient performance. The validity of the proposed control laws is substantiated through simulations and experiments.