Normal and tangent force neuro-fuzzy control of a soft-tip robot with unknown kinematics

Assuming that contact kinematics is known, there exists many force robot control schemes, however the common practice of placing a deformable pad at contact makes difficult its implementation. The difficulty stems from the fact that the this pad introduces contact kinematics uncertainties due to the unknown deformation. In this paper, considering the full non-linear constrained rigid robot equipped with a hemispherical soft-tip as end-effector, a force regulator is proposed. To compensate the kinematic uncertainty at contact, induced by the unknown soft-tip deformation, a multi-input single-output (MISO) self-tuning fuzzy-rule emulated neural network (MiFRENN) is used. Additionally, the gravity compensation together with a damping injection term in the controller are used to guarantee local convergence of the normal and tangential force errors at a given equilibrium. The stability domain of the system varying depending on the knowledge-based contribution of the MISO-MiFRENN and the damping injection, which amounts for a novel scheme that can be used for other advanced robotic contact tasks, such as tactile exploration, dexterous manipulation or biped locomotion. Representative simulations illustrate the closed-loop numerical behavior.