Erik D. Engeberg

Adaptive Sliding Mode Control for Prosthetic Hands to Simultaneously Prevent Slip and Minimize Deformation of Grasped Objects

Adaptive sliding mode and integral sliding mode grasped object slip prevention controllers are implemented for a prosthetic hand and compared to a proportional derivative shear force feedback slip prevention controller as well as a sliding mode controller without slip prevention capabilities. Slip of grasped objects is detected by band-pass filtering the shear force derivative to amplify high frequency vibrations that occur as the grasped object slides relative to the fingers. The integral sliding mode slip prevention controller provides a robust design framework for slip prevention while addressing the issue of reducing the amount of deformation that the grasped object experiences to prevent slip. Averaged results from bench top experiments show that the integral sliding mode slip prevention controller produces the least amount of deformation to the grasped object while simultaneously preventing the object from being dropped.

Hybrid Force–Velocity Sliding Mode Control of a Prosthetic Hand

Four different methods of hand prosthesis control are developed and examined experimentally. Open-loop control is shown to offer the least sensitivity when manipulating objects. Force feedback substantially improves upon open-loop control. However, it is shown that the inclusion of velocity and/or position feedback in a hybrid force-velocity control scheme can further improve the functionality of hand prostheses. Experimental results indicate that the sliding mode controller with force, position, and velocity feedback is less prone to unwanted force overshoot when initially grasping objects than the other controllers.

Bioinspired Sinusoidal Finger Joint Synergies for a Dexterous Robotic Hand to Screw and Unscrew Objects With Different Diameters

This paper addresses the complex task of unscrewing and screwing objects with a dexterous anthropomorphic robotic hand in two cases: with the first finger and thumb and also with the little finger and thumb. To develop an anthropomorphic solution, human finger synergies from nine test subjects were recorded while unscrewing and screwing a threaded cap. Human results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for application to a robotic hand. Because the joint trajectories exhibited the same frequency, a family of sinusoids that share a common time vector can be used in the path planning of the robotic hand to unscrew and screw objects. Additionally, the human unscrewing data are highly similar to the mirror image of the screwing data. This chiastic trait enables screwing to be performed by decreasing the time vector; increasing the time vector produces unscrewing. These factors significantly reduce the computational cost and complexity of the task. Cartesian and joint space error analyses show that the developed sinusoidal trajectories closely mimic the motion profiles seen in the human experiments. Furthermore, this bioinspired sinusoidal solution is extended to objects with wide variations in diameters by relating joint angle offsets of the robotic hand to object diameter size through the forward kinematics equations. The sinusoidal trajectories are all implemented within a PID sliding mode controller for a dexterous artificial hand to ensure overall system stability. Using the bioinspired sinusoidal joint angle trajectories, the robotic hand successfully unscrewed and screwed four different objects in all trials conducted with each object diameter size.

Improved Grasp Force Sensitivity for Prosthetic Hands Through Force-Derivative Feedback

Sensitivity of applied grasp force is improved for a myoelectrically controlled prosthetic hand under force control through normal force-derivative feedback. Benchtop experiments and results from 12 human test subjects indicate that normal force-derivative feedback can be used in prosthetic hands to help prevent accidental damage to delicate objects.

Backstepping and Sliding Mode Control Hybridized for a Prosthetic Hand

Open loop and force controllers are compared experimentally with three robust parallel force-velocity controllers that are developed for a prosthetic hand. Robust sliding mode, backstepping, and hybrid sliding mode-backstepping (HSMBS) parallel force-velocity controllers are tested by ten able-bodied subjects. Results obtained with a myoelectrically controlled prosthesis indicate that all three robust controllers offer a statistically significant improvement over linear hand prosthesis control schemes. The robust controllers enable the human operators to more easily manipulate a delicate object. Bench top experiments combined with quantitative and qualitative evaluations from ten test subjects reveal the HSMBS controller to be the best choice to improve control of powered prosthetic hands.

Enhanced visual feedback for slip prevention with a prosthetic hand

Background: Upper limb amputees have no direct sense of the grip force applied by a prosthetic hand; thus, precise control of the applied grip force is difficult for amputees. Since there is little object deformation when rigid objects are grasped, it is difficult for amputees to visually gauge the applied grip force in this situation. Objectives: To determine if the applied grip force from a prosthetic hand can be visually displayed and used to more efficaciously grasp objects. Study Design: Experimental controlled trial. Methods: Force feedback is used in the control algorithm for the prosthetic hand and supplied visually to the user through a bicolor LED experimentally mounted to the thumb. Several experiments are performed by able-bodied test subjects to rate the usefulness of the additional visual feedback when manipulating a clearly visible, brittle object that can break if grasped too firmly. A hybrid force-velocity sliding mode controller is used with and without additional visual force feedback supplied to the operators. Results: Subjective evaluations and success rates from the test subjects indicate a statistically significant reduction in breaking the grasped object when using the prosthesis with the extra visual feedback. Conclusions: The additional visual force feedback can effectively facilitate the manipulation of brittle objects. Clinical relevance The novel approach of this research is the implementation of a noninvasive, effective and economic technique to visually indicate the grip force applied by a prosthetic hand to upper limb amputees. This technique provides a statistically significant improvement when handling brittle objects.

A physiological basis for control of a prosthetic hand

Abstract Recent surveys from upper limb amputees indicate the sentiment that prosthetic hands do not function in a life-like manner and are not intuitively controlled. Thus, two methods of control for a prosthetic hand are presented. A proportional derivative (PD) force controller is compared to a novel biomimetic application of sliding mode control. The biomimetic sliding mode (BSM) controller was designed to map human muscle signals into prosthesis motor command signals in a physiologically expected manner. The BSM and PD controllers were evaluated analytically and subjectively by one amputee and nine nonamputee test subjects. The posture of the hands of the nonamputee test subjects were measured with a CyberGlove and used to determine if the position of the prosthesis (when driven by both controllers) was highly correlated to the posture of the human hands. Force tracking experiments were also performed by all test subjects with both controllers to evaluate the ability to control the applied force. Finally, a dual object lifting task was performed by all test subjects to determine if the mapping of electromyogram (EMG) signals with the BSM controller resulted in physiologically expected motions. A nonparametric Mann–Whitney U -test was performed on the subjective evaluations to determine the statistical significance of the evaluations. The BSM controller was shown to replicate the posture of the human hand much more accurately than the PD force controller. The BSM controller also enabled better average force tracking results and higher success rates with the dual object lifting experiment while the same task was nearly impossible to perform with the PD controller. Finally, the BSM controller was subjectively rated to be more similar to control in comparison to the human hand with respect to position and force.

Adaptive object slip prevention for prosthetic hands through proportional-derivative shear force feedback

Three different object slip prevention controllers are incorporated into a force feedback controller for a prosthetic hand. Proportional and proportional-derivative shear force feedback controllers are explored in addition to an adaptive slip prevention algorithm to update the controllerpsilas estimate on the coefficient of friction as slips occur. Bench top experiments reveal a statistically significant improvement of the adaptive slip prevention controller over the others. Results from eight human test subjects indicate that all three methods of slip prevention significantly improve upon force control.

Anthropomorphic finger antagonistically actuated by SMA plates.

Most robotic applications that contain shape memory alloy (SMA) actuators use the SMA in a linear or spring shape. In contrast, a novel robotic finger was designed in this paper using SMA plates that were thermomechanically trained to take the shape of a flexed human finger when Joule heated. This flexor actuator was placed in parallel with an extensor actuator that was designed to straighten when Joule heated. Thus, alternately heating and cooling the flexor and extensor actuators caused the finger to flex and extend. Three different NiTi based SMA plates were evaluated for their ability to apply forces to a rigid and compliant object. The best of these three SMAs was able to apply a maximum fingertip force of 9.01N on average. A 3D CAD model of a human finger was used to create a solid model for the mold of the finger covering skin. Using a 3D printer, inner and outer molds were fabricated to house the actuators and a position sensor, which were assembled using a multi-stage casting process. Next, a nonlinear antagonistic controller was developed using an outer position control loop with two inner MOSFET current control loops. Sine and square wave tracking experiments demonstrated minimal errors within the operational bounds of the finger. The ability of the finger to recover from unexpected disturbances was also shown along with the frequency response up to 7 rad s(-1). The closed loop bandwidth of the system was 6.4 rad s(-1) when operated intermittently and 1.8 rad s(-1) when operated continuously.

Anthropomorphic Control of a Dexterous Artificial Hand via Task Dependent Temporally Synchronized Synergies

Despite the recent influx of increasingly dexterous prostheses, there remains a lack of sufficiently intuitive control methods to fully utilize this dexterity. As a solution to this problem, a control framework is proposed which allows the control of an arbitrary number of Degrees of Freedom (DOF) through a single electromyogram (EMG) control input. Initially, the joint motions of nine test subjects were recorded while grasping and catching a cylinder. Inherent differences emerged depending upon whether the cylinder was grasped or caught. These data were used to form a distinct synergy for each task, described as the families of parametric functions of time that share a mutual time vector. These two Temporally Synchronized Synergies (TSS) were derived to reflect the task dependent control strategies adopted by the initial participants. These synergies were then mapped to a dexterous artificial hand that was subsequently controlled by two subjects with transradial amputations. The EMG signals from these subjects were used to replace the time vector shared by the synergies, enabling the subjects to perform both tasks with a dexterous artificial hand using only a single EMG input. After a ten minute training period, the subjects learned to use the dexterous artificial hand to grasp and catch the cylinder with 100.0% and 65.0% average success rates, respectively.