scholarly journals Control Framework for Sloped Walking With a Powered Transfemoral Prosthesis

2022 ◽  
Vol 15 ◽  
Author(s):  
Namita Anil Kumar ◽  
Shawanee Patrick ◽  
Woolim Hong ◽  
Pilwon Hur
Keyword(s):  
Impedance Control ◽  
Control Strategies ◽  
Slope Angle ◽  
Principal Component ◽  
Human Gait ◽  
Joint Control ◽  
Healthy Human ◽  
Control Framework ◽  
Slope Condition ◽  
Transfemoral Prosthesis

User customization of a lower-limb powered Prosthesis controller remains a challenge to this date. Controllers adopting impedance control strategies mandate tedious tuning for every joint, terrain condition, and user. Moreover, no relationship is known to exist between the joint control parameters and the slope condition. We present a control framework composed of impedance control and trajectory tracking, with the transitioning between the two strategies facilitated by Bezier curves. The impedance (stiffness and damping) functions vary as polynomials during the stance phase for both the knee and ankle. These functions were derived through least squares optimization with healthy human sloped walking data. The functions derived for each slope condition were simplified using principal component analysis. The weights of the resulting basis functions were found to obey monotonic trends within upslope and downslope walking, proving the existence of a relationship between the joint parameter functions and the slope angle. Using these trends, one can now design a controller for any given slope angle. Amputee and able-bodied walking trials with a powered transfemoral prosthesis revealed the controller to generate a healthy human gait. The observed kinematic and kinetic trends with the slope angle were similar to those found in healthy walking.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

scholarly journals Control Strategies for the Transition From Multijoint to Single-Joint Arm Movements Studied Using a Simple Mechanical Constraint

2000 ◽  
Vol 83 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Robert A. Scheidt ◽  
W. Zev Rymer
Keyword(s):  
Control Strategies ◽  
Human Subjects ◽  
Dynamic Performance ◽  
Activity Patterns ◽  
Adaptive Controller ◽  
Emg Activity ◽  
Joint Control ◽  
Primary Role ◽  
Joint Task ◽  
Normal Human

Changes were studied in neuromotor control that were evoked by constraining the motion of the elbow joint during planar, supported movements of the dominant arm in eight normal human subjects. Electromyograph (EMG) recordings from shoulder and arm muscles were used to determine whether the normal multijoint muscle activity patterns associated with reaching to a visual target were modified when the movement was reduced to a single-joint task, by pinning the elbow to a particular location in the planar work space. Three blocks of 150 movements each were used in the experiments. Subjects were presented with the unconstrained task in the first and third blocks with an intervening block of constrained trials. Kinematic, dynamic, and EMG measures of performance were compared across blocks. The imposition of the pin constraint caused predictable changes in kinematic performance, in that near-linear motions of the hand became curved. This was followed by changes in limb dynamic performance at the elbow. However, changes in EMG activity at the shoulder lagged the kinematic changes substantially (by about 15 trials). The gradual character of the changes in EMG timing does not support a primary role for segmental reflex action in mediating the transition between multijoint and single-joint control strategies. Furthermore, the scope and magnitude of these changes argues against the notion that human motor performance is driven by the optimization of muscle- or joint-related criteria alone. The findings are best described as reflecting the actions of a feedforward adaptive controller that has properties that are modified progressively according to the environmental state.

Myoelectric Torque Control of an Active Transfemoral Prosthesis During Stair Ascent

2011 ◽  
Author(s):  
Carl D. Hoover ◽  
Kevin B. Fite ◽  
George D. Fulk ◽  
Donald W. Holmes
Keyword(s):  
Impedance Control ◽  
Torque Control ◽  
Motion Capture Data ◽  
Clinical Tests ◽  
Joint Kinetics ◽  
Set Point ◽  
Stair Ascent ◽  
Normal Gait ◽  
Stiffness And Damping ◽  
Transfemoral Prosthesis

This paper presents experimental results of a myoelectric impedance controller designed for reciprocal stair ascent with an active-knee powered transfemoral prosthesis. The controller is modeled from non-amputee (normal) motion capture data, estimating knee torque with a linear two-state (stance/swing) impedance control form that includes proportional myoelectric torque control. The normal gait model is characterized by small stiffness and damping in both stance and swing, a low angle set-point in stance, a high angle set-point in swing, and proportional myoelectric control in stance but not swing. Clinical tests with a single unilateral transfemoral amputee indicate good performance of the controller; however, subject feedback suggests a reduction in the extensive myoelectric torque parameter and the need for constant, balanced myoelectric torque parameters in both stance and swing. Average prosthesis knee joint kinetics from a stairwell test using the amputee-tuned controller compare favorably with non-amputee gait data.

scholarly journals Patient-Centered Robot-Aided Passive Neurorehabilitation Exercise Based on Safety-Motion Decision-Making Mechanism

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Lizheng Pan ◽  
Aiguo Song ◽  
Suolin Duan ◽  
Zhuqing Yu
Keyword(s):  
Decision Making ◽  
Control Strategy ◽  
Position Control ◽  
Impedance Control ◽  
Control Strategies ◽  
Movement Speed ◽  
Stroke Patients ◽  
Patient Centered ◽  
Motion Speed ◽  
Clinical Experiments

Safety is one of the crucial issues for robot-aided neurorehabilitation exercise. When it comes to the passive rehabilitation training for stroke patients, the existing control strategies are usually just based on position control to carry out the training, and the patient is out of the controller. However, to some extent, the patient should be taken as a “cooperator” of the training activity, and the movement speed and range of the training movement should be dynamically regulated according to the internal or external state of the subject, just as what the therapist does in clinical therapy. This research presents a novel motion control strategy for patient-centered robot-aided passive neurorehabilitation exercise from the point of the safety. The safety-motion decision-making mechanism is developed to online observe and assess the physical state of training impaired-limb and motion performances and regulate the training parameters (motion speed and training rage), ensuring the safety of the supplied rehabilitation exercise. Meanwhile, position-based impedance control is employed to realize the trajectory tracking motion with interactive compliance. Functional experiments and clinical experiments are investigated with a healthy adult and four recruited stroke patients, respectively. The two types of experimental results demonstrate that the suggested control strategy not only serves with safety-motion training but also presents rehabilitation efficacy.

scholarly journals Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ji Ke ◽  
Yude Qin ◽  
Biao Wang ◽  
Shundong Yang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Principal Component ◽  
Building Energy ◽  
Physical Models ◽  
Data Driven ◽  
Building Energy Management

Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

scholarly journals Research on Energy Management Strategies of Extended-Range Electric Vehicles Based on Driving Characteristics

2020 ◽  
Vol 11 (3) ◽  
pp. 54 ◽  
Author(s):  
Yuanbin Yu ◽  
Junyu Jiang ◽  
Zhaoxiang Min ◽  
Pengyu Wang ◽  
Wangsheng Shen
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Management Strategy ◽  
Control Strategies ◽  
Management Strategies ◽  
Principal Component ◽  
Driving Cycle ◽  
Control Parameters ◽  
Energy Management Strategy ◽  
Extended Range

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS.

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