701 research outputs found
Physical Human-Robot Interaction Control of an Upper Limb Exoskeleton with a Decentralized Neuro-Adaptive Control Scheme
Within the concept of physical human-robot interaction (pHRI), the most
important criterion is the safety of the human operator interacting with a high
degree of freedom (DoF) robot. Therefore, a robust control scheme is in high
demand to establish safe pHRI and stabilize nonlinear, high DoF systems. In
this paper, an adaptive decentralized control strategy is designed to
accomplish the abovementioned objectives. To do so, a human upper limb model
and an exoskeleton model are decentralized and augmented at the subsystem level
to enable a decentralized control action design. Moreover, human exogenous
force (HEF) that can resist exoskeleton motion is estimated using radial basis
function neural networks (RBFNNs). Estimating both human upper limb and robot
rigid body parameters, along with HEF estimation, makes the controller
adaptable to different operators, ensuring their physical safety. The barrier
Lyapunov function (BLF) is employed to guarantee that the robot can operate in
a safe workspace while ensuring stability by adjusting the control law. Unknown
actuator uncertainty and constraints are also considered in this study to
ensure a smooth and safe pHRI. Then, the asymptotic stability of the whole
system is established by means of the virtual stability concept and virtual
power flows (VPFs) under the proposed robust controller. The experimental
results are presented and compared to proportional-derivative (PD) and
proportional-integral-derivative (PID) controllers. To show the robustness of
the designed controller and its good performance, experiments are performed at
different velocities, with different human users, and in the presence of
unknown disturbances. The proposed controller showed perfect performance in
controlling the robot, whereas PD and PID controllers could not even ensure
stable motion in the wrist joints of the robot
A Systematic Approach For Kinematic Design Of Upper Limb Rehabilitation Exoskeletons
Kinematic structure of an exoskeleton is the most fundamental block of its
design and is determinant of many functional capabilities of it. Although
numerous upper limb rehabilitation devices have been designed in the recent
years, there is not a framework that can systematically guide the kinematic
design procedure. Additionally, diversity of currently available devices and
the many minute details incorporated to address certain design requirements
hinders pinpointing the core kinematics of the available devices to compare
them against each other. This makes the review of literature for identifying
drawbacks of the state of the art systems a challenging and puzzling task. In
fact, lack of a unifying framework makes designing rehabilitation devices an
intuitive process and prone to biases from currently available designs. This
research work proposes a systematic approach for kinematic design of upper limb
rehabilitation exoskeletons based on conceptual design techniques. Having
defined a solution neutral problem statement based on the characteristics of an
ideal device, the main functionality of the system is divided into smaller
functional units via the Functional Decomposition Method. Various directions
for concept generation are explored and finally, it has been shown that a vast
majority of the current exoskeleton designs fit within the proposed design
framework and the defined functionalities
Inverse kinematics for upper limb compound movement estimation in exoskeleton-assisted rehabilitation
Robot-Assisted Rehabilitation (RAR) is relevant for treating patients affected by nervous system injuries (e.g., stroke and spinal cord injury) -- The accurate estimation of the joint angles of the patient limbs in RAR is critical to assess the patient improvement -- The economical prevalent method to estimate the patient posture in Exoskeleton-based RAR is to approximate the limb joint angles with the ones of the Exoskeleton -- This approximation is rough since their kinematic structures differ -- Motion capture systems (MOCAPs) can improve the estimations, at the expenses of a considerable overload of the therapy setup -- Alternatively, the Extended Inverse Kinematics Posture Estimation (EIKPE) computational method models the limb and Exoskeleton as differing parallel kinematic chains -- EIKPE has been tested with single DOFmovements of the wrist and elbow joints -- This paper presents the assessment of EIKPEwith elbow-shoulder compoundmovements (i.e., object prehension) -- Ground-truth for estimation assessment is obtained from an optical MOCAP (not intended for the treatment stage) -- The assessment shows EIKPE rendering a good numerical approximation of the actual posture during the compoundmovement execution, especially for the shoulder joint angles -- This work opens the horizon for clinical studies with patient groups, Exoskeleton models, and movements types -
Decentralized Nonlinear Control of Redundant Upper Limb Exoskeleton with Natural Adaptation Law
The aim of this work is to utilize an adaptive decentralized control method
called virtual decomposition control (VDC) to control the orientation and
position of the end-effector of a 7 degrees of freedom (DoF) right-hand
upper-limb exoskeleton. The prevailing adaptive VDC approach requires tuning of
13n adaptation gains along with 26n upper and lower parameter bounds, where n
is the number of rigid bodies. Therefore, utilizing the VDC scheme to control
high DoF robots like the 7-DoF upper-limb exoskeleton can be an arduous task.
In this paper, a new adaptation function, so-called natural adaptation law
(NAL), is employed to eliminate these burdens from VDC, which results in
reducing all 13n gains to one and removing dependency on upper and lower
bounds. In doing so, VDC-based dynamic equations are restructured, and inertial
parameter vectors are made compatible with NAL. Then, the NAL adaptation
function is exploited to design a new adaptive VDC scheme. This novel adaptive
VDC approach ensures physical consistency conditions for estimated parameters
with no need for upper and lower bounds. Finally, the asymptotic stability of
the algorithm is proved with the virtual stability concept and the accompanying
function. The experimental results are utilized to demonstrate the excellent
performance of the proposed new adaptive VDC scheme.Comment: Manuscript is published in 2022 IEEE-RAS 21st International
Conference on Humanoid Robots (Humanoids
Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking
This manuscript presents control of a high-DOF fully actuated lower-limb
exoskeleton for paraplegic individuals. The key novelty is the ability for the
user to walk without the use of crutches or other external means of
stabilization. We harness the power of modern optimization techniques and
supervised machine learning to develop a smooth feedback control policy that
provides robust velocity regulation and perturbation rejection. Preliminary
evaluation of the stability and robustness of the proposed approach is
demonstrated through the Gazebo simulation environment. In addition,
preliminary experimental results with (complete) paraplegic individuals are
included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses
reviewers' concerns about the robustness of the algorithm and the motivation
for using such exoskeleton
Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles
In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR
Biomechatronics: Harmonizing Mechatronic Systems with Human Beings
This eBook provides a comprehensive treatise on modern biomechatronic systems
centred around human applications. A particular emphasis is given to exoskeleton
designs for assistance and training with advanced interfaces in human-machine
interaction. Some of these designs are validated with experimental results which
the reader will find very informative as building-blocks for designing such systems.
This eBook will be ideally suited to those researching in biomechatronic area with
bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design
at post-graduate level
System Identification of Bipedal Locomotion in Robots and Humans
The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints
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