Content area
Full Text
Introduction
Commercial prostheses are typically velocity controlled or position controlled; no tactile system is integrated in the hand and the success of the grasp is based on the visual feedback of the amputee.[1],[2] On the other hand, control solutions of prosthetic hands based on tactile feedback are borrowed from robotics, where the tactile sensing allows endowing the robotic hands with autonomous dexterous manipulation features. In robotic applications, tactile systems are used for objects recognition tasks, control forces, grasp objects, and to servo surfaces.[3] The control approaches can control fingers torque, force, velocity, and trajectory and include classical proportional-integral-derivative (PID), adaptive, robust, neural, fuzzy sliding mode, and their combinations.[4],[5] In addition, a well-consolidated approach to ensure grasp stability relies on the concept of friction cone, thus implying that the ratio between the normal force and the tangential force during grasping, multiplied by the static coefficient of friction, has to exceed 1. This method is very effective; however, it suffers from some limitations that make it unsuitable for prosthetics, for example, it requires sensors able to measure both normal and tangential forces (although estimations of the latter can be used as in Wettels et al.[6] ) and also requires a priori knowledge on the static friction coefficient.
Alternative and more recent approaches able to still guarantee grasp stability rely on force control schemes that allow recognizing slippage events by the decrease in the measured normal force. An example of this approach can be found in Cipriani et al.,[7] where a PID control is used to preshape a multi-fingered underactuated hand, and a simultaneous force control is applied to all the fingers during grasping. In Jo et al.,[8] a proportional-integral (PI) force control with an inner velocity loop has been proposed, but no experimental tests on real prosthetic hands have been done. A hybrid approach has been tested in Zhu et al.,[9] where a PI force control is adopted for the outer loop, while a fuzzy position control is used in the inner loop. In Pasluosta and Chiu,[10] a control strategy based on a neural network has been implemented in order to compensate for sensors and hardware non-linearities. The control was divided into two stages: the former was a...