Slippage Sensory Feedback and Nonlinear Force Control System for a Low-Cost Prosthetic Hand

Abstract

The low acceptability of current prosthetic devices can be attributed to the extensive psychological effort and the high cost associated with them. To address these concerns, an on-line slippage detector was developed using only inexpensive force sensors placed at the tips of a prototype hand. The prototype consists of a five fingered prosthetic hand consisting of active digits driven independently by DC motors. Force sensor resistors (FSR) are placed at the tip of each active finger and potentiometers are attached at the proximal and middle joints. Using the information from the FSR, not only can we detect the level of normal force exerted but also slippage between the fingers and the object by calculating the fluctuations of the exerted force. An on-line algorithm is developed to calculate the derivative of the force and determine when slippage is produced. Nonlinear model predictive control (NMPC) is used to provide feedback control to the prosthetic device. It utilizes a neural network to model the dynamics of each finger. Using this model, it is possible to predict future plant performance (the amount of force exerted by the prosthetic hand). Consequently, the controller uses this prediction to calculate the best input (current needed to drive the actuators) for the system to obtain the desired output over a specific time horizon. In order to calculate the future control inputs, the optimization system minimizes the cost function associated with the difference between the measured force and the reference / target output. Experimental protocols involve grasping various objects and inducing slippage. Data was collected using the NI DAQ cards and LabVIEW software. Experiments showed promising results using this strategy in which the force exerted on an object can be modulated without additional efforts from the users.

Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

“…An important aspect of grip security is how the grip force is to be adjusted following gross slip detection to recover the grasp on the object. A common proposal is to simply increase the grip force in a predetermined way until slip is no longer detected [63][64][65][66][67][68][69].…”

A dual-loop control system of grasping force for prosthetic hands

“…e K , d K , 0 K and u K are the ratio coefficients. The FSR, whose functions are introduced in [9][10][11], is in charge of detecting grasping force and generating relative signals. With the grasping force signals gained at the first touch, the primary control system evaluates an expected force and gives instructions to the fuzzy controller, which is widely used for industrial process [12][13][14][15].…”

Evaluation of a Neural Network-Based Control Strategy for a Cost-Effective Externally-Powered Prosthesis

“…The hypothesis of this work is to combine a nonlinear force control system and a simple disturbance detection algorithm so that the nonlinearities associated with inexpensive components can be accommodated without jeopardizing the overall performance of the device. An easy to use, underactuated externally-powered prosthetic hand capable of producing power to precision grasping was designed and fabricated (Pasluosta et al 2009). Our group has previously designed an intelligent pattern recognition system as a high-level controller to classify electromyogram (EMG) signals from strategically placed sensors, allowing the selection of an appropriate gripping pattern (Fontana & Chiu, 2009).…”