Adaptive Control of Robotic Fingers for Grasping Stationary and Falling Soft Balls using Fuzzy Sugeno method

Acquiring appropriate tools adaptable to various tasks is the most fundamental feature of the robots dealing with environment. Therefore, it would be more beneficial to plan a mechanism amenable to control the robotic fingers. Since robotic fingers have completely nonlinear behavior and their modeling is associated with the difficulties arising from the factors such as friction, physical features of transmission mechanisms, and changes in hand’s orientation, adopting a model-independent method of control will be useful. In this paper, a Takagi-Sugeno-Kang (TSK) fuzzy controller, which adaptively updates its consequence parameters, is employed for position/force control of the fingertips grasping a light and soft ball. Designing in Cartesian space and being model-independent are some of the most important advantages of this method. In the first step, force and position reference values are calculated using a predetermined stationary grasping strategy. Afterward, the performance of the adaptive fuzzy TSK controller in maintaining the ball with and without the existence of force measurement noise and joint friction are evaluated. Furthermore, the process of catching a falling ball is divided into approaching, locking and holding phases. Finally, in the simulation section, it is shown that the adaptive fuzzy TSK controller is an efficient way for performing the aforementioned three phases.