Boundary control of flexible mechanical systems

Ph.DDOCTOR OF PHILOSOPH

A Review on Contact and Collision Methods for Multi-body Hydrodynamic problems in Complex Flows

Modeling and direct numerical simulation of particle-laden flows have a tremendous variety of applications in science and engineering across a vast spectrum of scales from pollution dispersion in the atmosphere, to fluidization in the combustion process, to aerosol deposition in spray medication, along with many others. Due to their strongly nonlinear and multiscale nature, the above complex phenomena still raise a very steep challenge to the most computational methods. In this review, we provide comprehensive coverage of multibody hydrodynamic (MBH) problems focusing on particulate suspensions in complex fluidic systems that have been simulated using hybrid Eulerian-Lagrangian particulate flow models. Among these hybrid models, the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) provides mathematically simple and computationally-efficient algorithms for solid-fluid hydrodynamic interactions in MBH simulations. This paper elaborates on the mathematical framework, applicability, and limitations of various 'simple to complex' representations of close-contact interparticle interactions and collision methods, including short-range inter-particle and particle-wall steric interactions, spring and lubrication forces, normal and oblique collisions, and mesoscale molecular models for deformable particle collisions based on hard-sphere and soft-sphere models in MBH models to simulate settling or flow of nonuniform particles of different geometric shapes and sizes in diverse fluidic systems.Comment: 37 pages, 12 Figure

Neuromechanical Tuning for Arm Motor Control

Movement is a fundamental behavior that allows us to interact with the external world. Its importance to human health is most evident when it becomes impaired due to disease or injury. Physical and occupational rehabilitation remains the most common treatment for these types of disorders. Although therapeutic interventions may improve motor function, residual deficits are common for many pathologies, such as stroke. The development of novel therapeutics is dependent upon a better understanding of the underlying mechanisms that govern movement. Movement of the human body adheres to the principles of classic Newtonian mechanics. However, due to the inherent complexity of the body and the highly variable repertoire of environmental contexts in which it operates, the musculoskeletal system presents a challenging control problem and the onus is on the central nervous system to reliably solve this problem. The neural motor system is comprised of numerous efferent and afferent pathways with a hierarchical organization which create a complex arrangement of feedforward and feedback circuits. However, the strategy that the neural motor system employs to reliably control these complex mechanics is still unknown. This dissertation will investigate the neural control of mechanics employing a “bottom-up” approach. It is organized into three research chapters with an additional introductory chapter and a chapter addressing final conclusions. Chapter 1 provides a brief description of the anatomical and physiological principles of the human motor system and the challenges and strategies that may be employed to control it. Chapter 2 describes a computational study where we developed a musculoskeletal model of the upper limb to investigate the complex mechanical interactions due to muscle geometry. Muscle lengths and moment arms contribute to force and torque generation, but the inherent redundancy of these actuators create a high-dimensional control problem. By characterizing these relationships, we found mechanical coupling of muscle lengths which the nervous system could exploit. Chapter 3 describes a study of muscle spindle contribution to muscle coactivation using a computational model of primary afferent activity. We investigated whether these afferents could contribute to motoneuron recruitment during voluntary reaching tasks in humans and found that afferent activity was orthogonal to that of muscle activity. Chapter 4 describes a study of the role of the descending corticospinal tract in the compensation of limb dynamics during arm reaching movements. We found evidence that corticospinal excitability is modulated in proportion to muscle activity and that the coefficients of proportionality vary in the course of these movements. Finally, further questions and future directions for this work are discussed in the Chapter 5

Research on hybrid manufacturing using industrial robot

The applications of using industrial robots in hybrid manufacturing overcome many restrictions of the conventional manufacturing methods, such as small part building size, long building period, and limited material choices. However, some problems such as the uneven distribution of motion accuracy within robot working volume, the acceleration impact of robot under heavy external loads, few methods and facilities for increasing the efficiency of hybrid manufacturing process are still challenging. This dissertation aims to improve the applications of using industrial robot in hybrid manufacturing by addressing following three categories research issues. The first research issue proposed a novel concept view on robot accuracy and stiffness problem, for making the maximum usage of current manufacturing capability of robot system. Based on analyzing the robot forward/inverse kinematic, the angle error sensitivity of different joint and the stiffness matrix properties of robot, new evaluation formulations are established to help finding the best position and orientation to perform a specific trajectory within the robot\u27s working volume. The second research issue focus on the engineering improvements of robotic hybrid manufacturing. By adopting stereo vision, laser scanning technology and curved surface compensation algorithm, it enhances the automation level and adaptiveness of hybrid manufacturing process. The third research issue extends the robotic hybrid manufacturing process to the broader application area. A mini extruder with a variable pitch and progressive diameter screw is developed for large scale robotic deposition. The proposed robotic deposition system could increase the building efficiency and quality for large-size parts. Moreover, the research results of this dissertation can benefit a wide range of industries, such as automation manufacturing, robot design and 3D printing --Abstract, page iv

Bio-inspired robotic joint and manipulator : from biomechanical experimentation and modeling to human-like compliant finger design and control

textOne of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.Mechanical Engineerin

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces