[THESIS] Model-based Control of Upper Extremity Human-Robot Rehabilitation Systems – Full Text PDF

Abstract

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to:

1) deliver repetitive and stimulating practice to post-stroke patients,

2) minimize therapist interventions, and

3) increase the number of patients per therapist, thereby decreasing
the associated cost.

The control of rehabilitation robots is often limited to black or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues.
This thesis presents advanced impedance- and model-based controllers for an endeffector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment.

In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is
developed. The rehabilitation robot is a 2 Degree-of-Freedom (DOF) parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Humanrobot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy.

In robotic rehabilitation, because of physical interaction of the patient with a mechanical
device, safety is a fundamental element in the design of a controller. Thus, impedance based assistance is commonly used for robotic rehabilitation. One of our objectives is
to achieve a reliable and real-time implementable controller. In our definition, a reliable
controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation.

Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed.

In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-Dimensional (3D) musculoskeletal model or internal 2-Dimensional (2D) musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2D musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project.[…]

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[REVIEW] Control Strategies for Active Lower Extremity Prosthetics and Orthotics – Full Text PGF

Abstract (provisional)

Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user. This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies. As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os — not as independent devices, but as actors within an ecosystem — is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers. Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use. The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL.

The complete article is available as a provisional PDF. The fully formatted PDF and HTML versions are in production.

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