scholarly journals Adapting Semi-Active Prostheses to Real-World Movements: Sensing and Controlling the Dynamic Mean Ankle Moment Arm with a Variable-Stiffness Foot on Ramps and Stairs

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6009
Author(s):  
Jennifer K. Leestma ◽  
Katherine Heidi Fehr ◽  
Peter G. Adamczyk
Keyword(s):  
Closed Loop ◽  
Variable Stiffness ◽  
Closed Loop Control ◽  
Control Input ◽  
Moment Arm ◽  
Loop Control ◽  
Ankle Moment ◽  
Active Devices ◽  
Machine Learning Model ◽  
Load Sensor

(1) Background: Semi-active prosthetic feet can provide adaptation in different circumstances, enabling greater function with less weight and complexity than fully powered prostheses. However, determining how to control semi-active devices is still a challenge. The dynamic mean ankle moment arm (DMAMA) provides a suitable biomechanical metric, as its simplicity matches that of a semi-active device. However, it is unknown how stiffness and locomotion modes affect DMAMA, which is necessary to create closed-loop controllers for semi-active devices. In this work, we develop a method to use only a prosthesis-embedded load sensor to measure DMAMA and classify locomotion modes, with the goal of achieving mode-dependent, closed-loop control of DMAMA using a variable-stiffness prosthesis. We study how stiffness and ground incline affect the DMAMA, and we establish the feasibility of classifying locomotion modes based exclusively on the load sensor. (2) Methods: Human subjects walked on level ground, ramps, and stairs while wearing a variable-stiffness prosthesis in low-, medium-, and high-stiffness settings. We computed DMAMA from sagittal load sensor data and prosthesis geometric measurements. We used linear mixed-effects models to determine subject-independent and subject-dependent sensitivity of DMAMA to incline and stiffness. We also used a machine learning model to classify locomotion modes using only the load sensor. (3) Results: We found a positive linear sensitivity of DMAMA to stiffness on ramps and level ground. Additionally, we found a positive linear sensitivity of DMAMA to ground slope in the low- and medium-stiffness conditions and a negative interaction effect between slope and stiffness. Considerable variability suggests that applications of DMAMA as a control input should look at the running average over several strides. To examine the efficacy of real-time DMAMA-based control systems, we used a machine learning model to classify locomotion modes using only the load sensor. The classifier achieved over 95% accuracy. (4) Conclusions: Based on these findings, DMAMA has potential for use as a closed-loop control input to adapt semi-active prostheses to different locomotion modes.

scholarly journals Implicit Euler Implementation of Twisting Controller and Super-Twisting Observer without Numerical Chattering: Precise Quasi-Static MEMS Mirrors Control

2019 ◽  
Vol 256 ◽  
pp. 03004 ◽  
Author(s):  
Dong Luo ◽  
Xiaogang Xiong ◽  
Shanhai Jin ◽  
Wei Chen
Keyword(s):  
Sliding Mode Control ◽  
Closed Loop ◽  
Sliding Mode ◽  
Industrial Applications ◽  
Euler Method ◽  
Open Loop ◽  
Closed Loop Control ◽  
Control Input ◽  
Loop Control ◽  
Mode Control

The quasi-static operations of MEMS mirror are very sensitive to undesired oscillations due to its very low damping. It has been shown that closed-loop control can be superior to reduce those oscillations than open-loop control in the literature. For the closed-loop control, the conventional way of implementing sliding mode control (SMC) algorithm is forward Euler method, which results in numerical chattering in the control input and output. This paper proposes an implicit Euler implementation scheme of super twisting observer and twisting control for a commercial MEMS mirror actuated by an electrostatic staggered vertical comb (SVC) drive structure. The famous super-twisting algorithm is used as an observer and twisting SMC is used as a controller. Both are discretized by an implicit Euler integration method, and their implementation algorithms are provided. Simulations verify that, as compared to traditional sliding mode control implementation, the proposed scheme reduces the chattering both in trajectory tracking output and control input in presence of model uncertainties and external disturbances. The comparison demonstrates the potential applications of the proposed scheme in industrial applications in terms of feasibility and performance.

Research on the Cooperative Control Method for Trajectory Tracking of Automatic Guided Vehicle Based on Dynamic Position/Force

2013 ◽  
Vol 457-458 ◽  
pp. 1298-1302 ◽  
Author(s):  
Xuan Zuo Liu ◽  
Qiao Yun Yan ◽  
Fei Yun Tang
Keyword(s):  
Trajectory Tracking ◽  
Cooperative Control ◽  
Closed Loop ◽  
Control Structure ◽  
Sliding Mode ◽  
Closed Loop Control ◽  
Control Input ◽  
External Interference ◽  
Loop Control ◽  
Automatic Guided Vehicle

AbstractConsidering the influence of the dynamic characteristic of automatic guided vehicle (AGV) on trajectory tracking controlling, double closed loop control structure is proposed to realize the position/force cooperative control. The outer loop controlling uses backstepping to design corresponding position controller for kinematics model of AGV, while the inner control uses the integral sliding mode controlling. Self-adaptive controlling law is used to estimate the uncertain external interference in the driving force controller and stability of AGV trajectories tracking proof is proposed. In order to make the system achieve better control performance and prevent the occurrence of severe wobble, the hyperbolic tangent function in the control law of sliding mode control replaces the sign function to ensure a continuously smooth control input and states of the system. In the Matlab/simulink environment, tracking a given splayed trajectory generated by the S function to verify the double closed loop control structure and the effectiveness of the control algorithm proposed in this paper.

Linear Closed-Loop Control of Fluid Instabilities and Noise-Induced Perturbations: A Review of Approaches and Tools1

2016 ◽  
Vol 68 (2) ◽  
Author(s):  
Denis Sipp ◽  
Peter J. Schmid
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Control Input ◽  
Input Output ◽  
Control Laws ◽  
Loop Control ◽  
Stability Robustness ◽  
Data Control ◽  
Galerkin Models

This review article is concerned with the design of linear reduced-order models and control laws for closed-loop control of instabilities in transitional flows. For oscillator flows, such as open-cavity flows, we suggest the use of optimal control techniques with Galerkin models based on unstable global modes and balanced modes. Particular attention has to be paid to stability–robustness properties of the control law. Specifically, we show that large delays and strong amplification between the control input and the estimation sensor may be detrimental both to performance and robustness. For amplifier flows, such as backward-facing step flow, the requirement to account for the upstream disturbance environment rules out Galerkin models. In this case, an upstream sensor is introduced to detect incoming perturbations, and identification methods are used to fit a model structure to available input–output data. Control laws, obtained by direct inversion of the input–output relations, are found to be robust when applied to the large-scale numerical simulation. All the concepts are presented in a step-by-step manner, and numerical codes are provided for the interested reader.

A Multi-Purpose Temperature Control Method for MEMS Microheaters Without a Separate Temperature Sensor

2006 ◽  
Author(s):  
Byunghoon Bae ◽  
Junghoon Yeom ◽  
Bruce R. Flachsbart ◽  
Yanjun Tang ◽  
Richard I. Masel ◽  
...  
Keyword(s):  
Temperature Sensor ◽  
Closed Loop ◽  
Control Method ◽  
Electrical Heating ◽  
Thermal Mass ◽  
Closed Loop Control ◽  
Control Input ◽  
Response Delay ◽  
Loop Control ◽  
Finite Distance

In this paper, a temperature-controlled method that does not use a separate temperature sensor is presented for different MEMS electrical resistance heaters. Instead of using a Resistance Temperature Detectors (RTD) sensor or micro-thermocouple for closed-loop control of the temperature, which will have a finite distance between the heater and sensor and a response delay due to the thermal mass of the substrate on which the sensor resides, we use the change in resistance with temperature of the electrical heating element itself for the control input.

scholarly journals Roboticizing fabric by integrating functional fibers

2020 ◽  
Vol 117 (41) ◽  
pp. 25360-25369
Author(s):  
Trevor L. Buckner ◽  
R. Adam Bilodeau ◽  
Sang Yup Kim ◽  
Rebecca Kramer-Bottiglio
Keyword(s):  
Closed Loop ◽  
Variable Stiffness ◽  
Closed Loop Control ◽  
Load Bearing ◽  
Loop Control ◽  
Textile Manufacturing ◽  
Deployable Structure ◽  
Recent Emergence ◽  
Manufacturing Techniques

Fabrics are ubiquitous materials that have conventionally been passive assemblies of interlacing, inactive fibers. However, the recent emergence of active fibers with actuation, sensing, and structural capabilities provides the opportunity to impart robotic function into fabric substrates. Here we present an implementation of robotic fabrics by integrating functional fibers into conventional fabrics using typical textile manufacturing techniques. We introduce a set of actuating and variable-stiffness fibers, as well as printable in-fabric sensors, which allows for robotic closed-loop control of everyday fabrics while remaining lightweight and maintaining breathability. Finally, we demonstrate the utility of robotic fabrics through their application to an active wearable tourniquet, a transforming and load-bearing deployable structure, and an untethered, self-stowing airfoil.

The Impact of Visual Feedback Type on the Mastery of Visuo-Motor Transformations

2012 ◽  
Vol 220 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Sandra Sülzenbrück
Keyword(s):  
Empirical Research ◽  
Visual Feedback ◽  
Closed Loop ◽  
Motor Planning ◽  
Visual Input ◽  
Closed Loop Control ◽  
Loop Control ◽  
Feedback Type ◽  
Effective Use ◽  
The Impact

For the effective use of modern tools, the inherent visuo-motor transformation needs to be mastered. The successful adjustment to and learning of these transformations crucially depends on practice conditions, particularly on the type of visual feedback during practice. Here, a review about empirical research exploring the influence of continuous and terminal visual feedback during practice on the mastery of visuo-motor transformations is provided. Two studies investigating the impact of the type of visual feedback on either direction-dependent visuo-motor gains or the complex visuo-motor transformation of a virtual two-sided lever are presented in more detail. The findings of these studies indicate that the continuous availability of visual feedback supports performance when closed-loop control is possible, but impairs performance when visual input is no longer available. Different approaches to explain these performance differences due to the type of visual feedback during practice are considered. For example, these differences could reflect a process of re-optimization of motor planning in a novel environment or represent effects of the specificity of practice. Furthermore, differences in the allocation of attention during movements with terminal and continuous visual feedback could account for the observed differences.

118-LB: Glucose Variability throughout the Menstrual Cycle on Closed-Loop Control in Type 1 DM

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 118-LB
Author(s):  
CAROL J. LEVY ◽  
GRENYE OMALLEY ◽  
SUE A. BROWN ◽  
DAN RAGHINARU ◽  
YOGISH C. KUDVA ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Closed Loop ◽  
Glucose Variability ◽  
Closed Loop Control ◽  
Loop Control ◽  
Type 1 Dm

101-LB: Eighteen-Month Use of Closed-Loop Control (CLC): A Randomized, Controlled Trial

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 101-LB
Author(s):  
SUE A. BROWN ◽  
DAN RAGHINARU ◽  
BRUCE A. BUCKINGHAM ◽  
YOGISH C. KUDVA ◽  
LORI M. LAFFEL ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Closed Loop ◽  
Controlled Trial ◽  
Closed Loop Control ◽  
Loop Control ◽  
Randomized Controlled
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