Stochastic recruitment strategies for controlling artificial muscles

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 171-176).This thesis presents a new architecture for controlling active material actuators inspired by biological motor recruitment. An active material is broken down into many small fibers and grouped together to form one large actuator. Each of these fibers is held in a binary state, either relaxed or contracted, using a small local controller which responds to a broadcast input signal from a central controller. The output force and displacement of the actuator is a function of the number of contracted fibers at any point in time. This architecture enables the creation of large-scale, controllable actuators from highly non-linear active materials. The key innovation enabling the central controller to coordinate the behavior of very many small identical units is to randomize the behavior of each unit. This thesis explains how a collection of active material motor units responding in a random, uncorrelated fashion to broadcast commands will exhibit a predictable response that can be stabilized with feedback control and observed using a Kalman filter. Various control strategies will be presented and discussed, including open-loop plant behavior, linear feedback, optimal control, and model-based look-ahead control. Performance metrics such as accuracy and convergence time will be analyzed using dynamic programming and other control techniques. Parallels will also be discussed between this control problem and similar control problems in the field of swarm robotics.(cont.) The stochastic, recruitment-like actuator architecture is demonstrated in shape memory alloy actuators, each composed of 60 individual elements, having a displacement of over 20 mm and a peak force of over 100 N. Control of displacement, isometric force and stiffness are demonstrated using the observer-controller framework. Two actuators are used in an antagonistic fashion to control the stiffness and position of a 1-DOF arm joint.by Lael Ulam Odhner.Sc.D

Dynamic Cellular Actuator Arrays and Expanded Fingerprint Method for Dynamic Modeling

Copyright © ElsevierDOI: http://dx.doi.org/10.1016/j.robot.2013.06.013A key step to understanding and producing natural motion is creating a physical, well understood actuator with a dynamic model resembling biological muscle. This actuator can then serve as the basis for building viable, full-strength, and safe muscles for disabled patients, rehabilitation, human force amplification, telerobotics, and humanoid robotic systems. This paper presents a cell-based flexible actuator modeling methodology and the General Fingerprint Method for systematically and efficiently calculating the actuators’ respective dynamic equations of motion. The cellular actuator arrays combine many flexible ‘cells’ in complex and varied topologies for combined large-scale motion. The cells can have varied internal dynamic models and common actuators such as piezoelectric, SMA, linear motor, and pneumatic technologies can fit the model by adding a flexible element in series with the actuator. The topology of the cellular actuator array lends it many of its properties allowing the final muscle to be catered to particular applications. The General Fingerprint Method allows for fast recalculation for different and/or changing structures and internal dynamics, and provides an intuitive base for future controls work. This paper also presents two physical SMA based cellular actuator arrays which validate the presented theory and give a basis for future development

Acoustic Communication for Medical Nanorobots

Communication among microscopic robots (nanorobots) can coordinate their activities for biomedical tasks. The feasibility of in vivo ultrasonic communication is evaluated for micron-size robots broadcasting into various types of tissues. Frequencies between 10MHz and 300MHz give the best tradeoff between efficient acoustic generation and attenuation for communication over distances of about 100 microns. Based on these results, we find power available from ambient oxygen and glucose in the bloodstream can readily support communication rates of about 10,000 bits/second between micron-sized robots. We discuss techniques, such as directional acoustic beams, that can increase this rate. The acoustic pressure fields enabling this communication are unlikely to damage nearby tissue, and short bursts at considerably higher power could be of therapeutic use.Comment: added discussion of communication channel capacity in section

Modeling & Characterizing Stochastic Actuator Arrays

©2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Presented at the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 11-15, 2009, St. Louis, USA.DOI: 10.1109/IROS.2009.5354645If the modal response for a single degree of freedom flexible system is known, a command generation architecture can be determined which schedules on/off actuator effort such that the resulting motion will have zero vibration. If the system possesses redundant on/off actuation, the number of possible zero vibration commands increases. Of particular interest is the command that has the minimum number of actuator changes in state. This paper presents how to determine this command and applies it in simulation to a flexible actuator inspired by human muscle

Controlling Biological Systems: The Lactose Regulation System of Escherichia Coli

In this paper we present a comprehensive framework for abstraction and controller design for a biological system. The first half of the paper concerns modeling and model abstraction of the system. Most models in systems biology are deterministic models with ordinary differential equations in the concentration variables. We present a stochastic hybrid model of the lactose regulation system of E. coli bacteria that capture important phenomena which cannot be described by continuous deterministic models. We then show that the resulting stochastic hybrid model can be abstracted into a much simpler model, a two-state continuous time Markov chain. The second half of the paper discusses controller design for a specific architecture. The architecture consists of measurement of a global quantity in a colony of bacteria as an output feedback, and manipulation of global environmental variables as control actuation. We show that controller design can be performed on the abstracted (Markov chain) model and implementation on the real model yields the desired result

MicroBioRobots for Single Cell Manipulation

One of the great challenges in nano and micro scale science and engineering is the independent manipulation of biological cells and small man-made objects with active sensing. For such biomedical applications as single cell manipulation, telemetry, and localized targeted delivery of chemicals, it is important to fabricate microstructures that can be powered and controlled without a tether in fluidic environments. These microstructures can be used to develop microrobots that have the potential to make existing therapeutic and diagnostic procedures less invasive. Actuation can be realized using various different organic and inorganic methods. Previous studies explored different forms of actuation and control with microorganisms. Bacteria, in particular, offer several advantages as controllable micro actuators: they draw chemical energy directly from their environment, they are genetically modifiable, and they are scalable and configurable in the sense that any number of bacteria can be selectively patterned. Additionally, the study of bacteria inspires inorganic schemes of actuation and control. For these reasons, we chose to employ bacteria while controlling their motility using optical and electrical stimuli. In the first part of the thesis, we demonstrate a bio-integrated approach by introducing MicroBioRobots (MBRs). MBRs are negative photosensitive epoxy (SU8) microfabricated structures with typical feature sizes ranging from 1-100 μm coated with a monolayer of the swarming Serratia marcescens. The adherent bacterial cells naturally coordinate to propel the microstructures in fluidic environments, which we call Self-Actuation. First, we demonstrate the control of MBRs using self-actuation, DC electric fields and ultra-violet radiation and develop an experimentally-validated mathematical model for the MBRs. This model allows us to to steer the MBR to any position and orientation in a planar micro channel using visual feedback and an inverted microscope. Examples of sub-micron scale transport and assembly as well as computer-based closed-loop control of MBRs are presented. We demonstrate experimentally that vision-based feedback control allows a four-electrode experimental device to steer MBRs along arbitrary paths with micrometer precision. At each time instant, the system identifies the current location of the robot, a control algorithm determines the power supply voltages that will move the charged robot from its current location toward its next desired position, and the necessary electric field is then created. Second, we develop biosensors for the MBRs. Microscopic devices with sensing capabilities could significantly improve single cell analysis, especially in high-resolution detection of patterns of chemicals released from cells in vitro. Two different types of sensing mechanisms are employed. The first method is based on harnessing bacterial power, and in the second method we use genetically engineered bacteria. The small size of the devices gives them access to individual cells, and their large numbers permit simultaneous monitoring of many cells. In the second part, we describe the construction and operation of truly micron-sized, biocompatible ferromagnetic micro transporters driven by external magnetic fields capable of exerting forces at the pico Newton scale. We develop micro transporters using a simple, single step micro fabrication technique that allows us to produce large numbers in the same step. We also fabricate microgels to deliver drugs. We demonstrate that the micro transporters can be navigated to separate single cells with micron-size precision and localize microgels without disturbing the local environment

Stochastic modeling and control of neural and small length scale dynamical systems

Recent advancements in experimental and computational techniques have created tremendous opportunities in the study of fundamental questions of science and engineering by taking the approach of stochastic modeling and control of dynamical systems. Examples include but are not limited to neural coding and emergence of behaviors in biological networks. Integrating optimal control strategies with stochastic dynamical models has ignited the development of new technologies in many emerging applications. In this direction, particular examples are brain-machine interfaces (BMIs), and systems to manipulate submicroscopic objects. The focus of this dissertation is to advance these technologies by developing optimal control strategies under various feedback scenarios and system uncertainties. Brain-machine interfaces (BMIs) establish direct communications between living brain tissue and external devices such as an artificial arm. By sensing and interpreting neuronal activity to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects such as amputees. However, lack of the incorporation of sensory feedback, such as proprioception and tactile information, from the artificial arm back to the brain has greatly limited the widespread clinical deployment of these neuroprosthetic systems in rehabilitation. In the first part of the dissertation, we develop a systematic control-theoretic approach for a system-level rigorous analysis of BMIs under various feedback scenarios. The approach involves quantitative and qualitative analysis of single neuron and network models to the design of missing sensory feedback pathways in BMIs using optimal feedback control theory. As a part of our results, we show that the recovery of the natural performance of motor tasks in BMIs can be achieved by designing artificial sensory feedbacks in the proposed optimal control framework. The second part of the dissertation deals with developing stochastic optimal control strategies using limited feedback information for applications in neural and small length scale dynamical systems. The stochastic nature of these systems coupled with the limited feedback information has greatly restricted the direct applicability of existing control strategies in stabilizing these systems. Moreover, it has recently been recognized that the development of advanced control algorithms is essential to facilitate applications in these systems. We propose a novel broadcast stochastic optimal control strategy in a receding horizon framework to overcome existing limitations of traditional control designs. We apply this strategy to stabilize multi-agent systems and Brownian ensembles. As a part of our results, we show the optimal trapping of an ensemble of particles driven by Brownian motion in a minimum trapping region using the proposed framework

Structural Systems Inspired by the Architecture of Skeletal Muscle

Modern engineering applications call for structural and material systems that exhibit advanced performance. To achieve this performance, researchers often look to nature for inspiration. Skeletal muscle is a multifunctional system with remarkable versatility and robustness, offering a great example on how to effectively store, convert, and release energy for force generation and shape change. To date, most efforts seeking to emulate muscle have focused on its bulk characteristics. However, it has recently been shown that many of muscle’s advantageous properties arise from the assembly and geometry of its microscale constituents. This dissertation will aim to develop new concepts for structural and material systems inspired by a fundamental understanding of the assembly of muscle’s constituent elements into contractile units. This is achieved by exploiting two key ingredients expressed by these constituents: metastability, which is the existence of multiple stable conformations for a prescribed global geometry, and ¬¬local conformation changes to switch between these stable topologies. Rather than faithfully emulating or seeking to explain the complex chemo-mechanical processes that govern muscle contraction, the major contributions of this thesis arise from the exploitation of the aforementioned key features within the context of engineered structures and materials systems. First, a fundamental metastable unit is studied under harmonic excitation. Experimental, numerical, and analytical investigations uncover the coexistence of multiple response regimes with significantly different amplitudes. These distinct regimes are exploited to achieve highly adaptable energy dissipation characteristics that vary by up to two orders of magnitude among them, even as excitation parameters are held constant. On the other hand, introducing asymmetry by varying a static bias parameter allows for smooth, finer variation of energy dissipation performance. Then, inspired by the ability of the myofibril lattice in skeletal muscle to trap strain energy that can be released on-demand, this thesis explores structural systems that leverage asymmetric multistability for energy capture and storage. The initial kinetic energy from impulsive excitation is shown to trigger state transitions that result in the capture of recoverable strain energy in higher-potential states. Reverse transitions to lower-energy states exploit this stored energy to facilitate efficient deployment and length change in the structure. Lastly, the effect of myofibril lattice spacing in skeletal muscle, and shear-like motions of adjacent filaments during contraction, serves as inspiration for the development of an architected modular material system that uses transverse confinements in conjunction with oblique, shear-like motions to give rise to sudden state transitions. Numerical results provide insight into the experimentally-observed behaviors, revealing that these energy-releasing transitions correspond to discrete changes in reaction force magnitude and direction Mechanical response properties can be tailored by strategic variation of transverse confinement and system geometry. Analytical tools using relatively simple models are developed to offer meaningful prediction of the above features. The overall outcomes of this thesis reveal great potential to develop high-performance, versatile, and adaptable structural and material systems by exploiting fundamental features of skeletal muscle architecture.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145893/1/kidambi_1.pd