Tetsuya Iwasaki, Ph.D. - US grants

[unreadable] DESCRIPTION (provided by applicant): The broad aim for this project is to enhance the understanding of biological information processing mechanisms. The immediate goals are to uncover the fundamental roles of sensory feedback mechanisms in the neuronal control of animal locomotion and to establish mathematical models that predict the dynamical behavior of and supply missing information about the biological system. More specifically, the aims are to I) perform biophysical and physiological experiments on leech preparations to collect neuronal and mechanical input/output data needed for quantitative models, II) develop a mathematical model of the neuronal control system for leech swimming that includes sensory feedback, III) predict the effects of sensory feedback through numerical simulations of the model, and IV) test these predictions through physiological experiments on leech preparations. [unreadable] [unreadable] This project employs the Lur'e model for neuronal dynamics, recently developed by the Pl, as a basis for the modeling of neurodynamic feedback control system of leech swimming. The class of Lur'e systems has been extensively studied in the systems and control discipline and thus a variety of mathematical analysis tools are available. The project develops dynamical models (differential equations) of the leech locomotion control system, consisting of the central oscillator, muscle actuation by motoneurons, body-fluid interactions, and sensory feedback from stretch receptors, through parameter identifications based on experimental observations. Physiological experiments will be conducted on dissected and intact leech preparations to obtain explicit values for model parameters and to test hypotheses generated by experiments performed on the model. [unreadable] [unreadable] The collaborative engineering--neurophysiological research proposed here is part of a broad effort to discover general principles for the neuronal control of animal movements. Because of the functional similarity, insights gained from the proposed research on leech swimming can be expected to increase our understanding of the neuronal control of rhythmic movements generally. Potential applications of the knowledge to be generated include insights into the cause of walking disability and development of rehabilitation methodologies. [unreadable] [unreadable]

The project will develop an understanding of the mechanism of animal locomotion from biological observations at the neuronal level, hypothesize the knowledge as engineering principles for feedback control design, and establish a systematic procedure for designing neurodynamic controllers to achieve optimally efficient autonomous locomotion. A simple yet accuratemathematical model for neuronal dynamics is developed first from a control perspective. Lyapunov-based methods will then be employed to analyse the oscillation properties of neuronal circuits to drive mechanical systems for locomotion. A prototype mechanical rectifier is used to experimentally validate the design principles to be developed for locomotion control.

The next technological revolution seems to rely on our understanding of complex biological systems. Such understanding would enable us to develop a completely new kind of robust, adaptive, and autonomous machines. The intellectual merit of this project is the basic understanding of the mechanism underlying such sophistication, through formalization of biological knowledge on animal locomotion in terms of engineering language of feedback control. The impacts of this research include innovations in a variety of fields. Realizations of new type of robotic locomotion systems for space explorations would be a direct application. Physiological control of the heart beat profile for stabilization of defective heart may be another application.

Objective: The overall goal of this career proposal is to explore the purely interdisciplinary subject
of feedback control system design for generation of rhythmic patterns found in animal locomotion,
and to develop effective pedagogical methods for inspiring engineering students with benefits of
biological knowledge in the context of systems and control.
Specific Goals:
I. Develop an orbital trajectory analysis method for a class of nonlinear systems arising from
modeling of biological pattern generators.
II. Establish a method for designing a dynamical system that achieves pattern generation with a
prescribed oscillation profile.
III. Revise the dynamics and control curriculum to provide a broader view of systems through
crossdisciplinary training in neuroscience.
IV. Integrate the bio-control research into the science and engineering education at broad levels
including K-12 and college undergraduate.
V. Broadly disseminate the results of the proposed activities.
Methods and Procedures (Keyed to the Specific Goals):
I. Mathematically formulate an oscillation analysis problem and derive a solution using tools
from robust control theory. Examine the degree of conservatism exploiting the biological knowl-edge
of particular central pattern generator (CPG) architectures.
II. Use the analysis result in I as a basis for developing a general theory for the design of CPGs
utilizing linear matrix inequality methods. Test the applicability of the design conditions against
the CPGs known from biology.
III. Improve an undergraduate course on systems modeling by introducing neuronal modeling
and pattern generation mechanism. Develop a new graduate course on robust and nonlinear control
for regulation and biological oscillation.
IV. Develop research-based educational tools through multidisciplinary, teamworking, senior
thesis projects. Use the tools to stimulate K-12 and undergraduate students' intellectual curiosity
into engineering and science through a series of outreach/educational activities.
V. Utilize conference presentations, publications in archival journals, invited seminars, and
possibly tutorial workshops in major international conferences.
Intellectual Merit: The central theme of feedback control theory has been the regulation around an
equilibrium point of a dynamical system. In contrast, much less attention has been paid to control
specifications involving periodic motion (or oscillation) generation despite their practical impor-tance.
The basic research outlined in this proposal will provide an initial stepping stone toward a
new control paradigm that focuses on autonomous pattern generation by feedback dynamics. In
particular, the project will establish a systematic method for analysis and design of pattern genera-tors
through the exploitation of biological knowledge on neuronal oscillators.
Broader Impact: The crossdisciplinary research activities over biology and control fields are in-tegrated
into educational activities at all levels. The revised curriculum will provide undergraduate
and graduate students with a broader perspective on engineering problems. The senior thesis project
will develop students with team-oriented problem-solving skills in a multidisciplinary environment.
The outreach activities will contribute to improve scientific literacy of K-12 students.

The objective of this research is to develop and demonstrate a commercially viable technique for self-sensing in active magnetic bearings by realizing a virtual position probe using measured amplifier voltage and current. Two potential approaches will be pursued. The first treats the system comprised of amplifier, magnetic actuator, and rotor journal as a linear plant parameterized by rotor position: a parameter observer takes advantage of persistency of excitation provided by switching amplifiers to determine this parameter (rotor position). The second approach exploits a linear periodic model of the full rotor in combination with the actuator and constructs a state observer for this system. Virtual probes (observers) based on both approaches will be implemented either in hardwired electronic form or, ideally, as a digital field programmable gate array. In either case, the performance of the resulting estimation (virtual probe) hardware will be examined using an existing magnetic bearing supported rotor and compared to conventional eddy current type proximitors.

The principal benefit of this research is to reduce the complexity of hardware in active magnetic bearings. By removing components from the harsh environment to which the bearings are exposed, the cost and reliability of these devices can be improved substantially. This greatly enhances the application of magnetic bearings to a number of important technologies ranging from micro gas turbines for distributed electric power generation to implantable artificial hearts for treatment of congestive heart failure and other similar cardiac ailments. Hardware complexity and reliability is a major obstacle to large-scale commercial use of magnetic bearings in these and many other applications: self-sensing is a pivotal step in reducing these problems. A tantalizing long term benefit and central goal of continuation of this research is development of truly "off-the-shelf" magnetic bearings which can be marketed in the same manner as conventional fluid film or rolling element bearings. By eliminating the position sensor, a significant hurdle is removed in approaching a critical "passivity" property which ensures that the bearing will stably support any stable rotor. This would mean that active magnetic bearings could be sold without tailoring them to each specific application, avoiding very costly engineering effort and stimulating the economies of scale critical to unlocking the nascent commercial potential of active magnetic bearing technology.

The overall goal of this project is to understand the biological control mechanism underlying natural rhythmic movements observed during animal locomotion, and to establish a design principle for engineering applications. In particular, we focus on the distributed control mechanism realized by neuronal oscillators called the central pattern generators (CPGs), and aim to establish a design theory for CPG-based feedback controllers to robustly achieve natural rhythmic movements of mechanical systems. We hypothesize that entrainment to a natural mode of oscillation can be robustly achieved for the closed-loop system by placing a CPG unit in the feedback loop between each collocated sensor/actuator pair of a multi-degree-of-freedom, lightly damped, mechanical system. We will develop analytical formulas for the control design parameters to achieve an oscillation with a given frequency and mode shape, using the method of multivariable harmonic balance (MHB). We will then examine whether the hypothesis is supported by the predictions from the MHB analysis.

Feedback controls are essential for many engineering applications in which we desire to adjust movements of a physical system. Current technologies enable extremely fast and accurate motions with the aid of computer-controls. However, such control devices are only good at following a command, and lack sophistication to "think" what to do when the situation changes. For instance, we would walk differently on ice so that we don't slip or fall, but a walking robot would not be able to adjust its motion. This is because the motion is planned ahead assuming a nominal situation, and a controller forces the robot to move as planned even when the situation changes. In biological systems, appropriate motions are planned in real time by CPGs, based on sensory feedback information. This research will uncover the biological mechanism underlying this integration of motion planning and controls, enabling designs of robust and adaptable engineering systems. The synergistic effect between neuroscience and control engineering will accelerate advance of both fields. In particular, the knowledge from neuroscience will help to uncover engineering principles, while the results from control engineering will in turn provide new predictions for biological mechanisms that can be tested through physiological experiments.

This basic research aims to establish a general theory for designing feedback control mechanisms to drive robotic systems that swim like fishes or crawl like snakes. The design method will enable propulsion with agility and energy efficiency. The control algorithm is inspired by the central pattern generator (CPG) --- neuronal circuits that command muscle contractions to achieve rhythmic body movements during animal locomotion. The CPG is an interconnection of multiple neurons with simple individual dynamics, exhibiting a collective behavior perceived as a pattern. What makes CPGs an attractive object for engineering applications is its ability to adaptively choose the pattern of body oscillation appropriate for varying environments. This exploratory research will investigate the potential of the CPG architecture to provide a viable foundation for a new system design methodology to achieve coordinated oscillations of mechanical systems by feedback control.

Understanding of the mechanisms underlying emergent behaviors of CPGs could provide a central idea for innovative design of engineered systems with new functionalities. A theory that relates local interactions to the resulting global pattern would help identify, predict, or avoid, for instance, traffic congestion and instability in power grids. Synergistic effects between neuroscience and control engineering will be exploited in both research and education. The educational goal of this project is to provide students with a broad dynamical systems view point that applies not only to the design of engineered machines but also to understanding of biological phenomena. The goal will be approached through multidisciplinary training of graduate and undergraduate students in a teamwork environment, and by incorporating research findings into control engineering courses. The results will be broadly disseminated to both neuroscience and control communities through conference presentations, journal publications, invited seminars, and tutorial workshops, to enhance cross-cultural fertilizations.

The goal of the proposed project is to understand whether and how neuronal control circuits exploit mechanical resonance during animal locomotion to achieve highly efficient rhythmic body movements. General principles will be uncovered through a specific case study of dynamical mechanisms underlying undulatory swimming of leeches. Diverse behaviors of leeches are generated by relatively simple neuronal circuits, providing a unique opportunity for preliminary understanding of how the brain works in the most primitive form. A simple integrated model of leech swimming, amenable to analytical studies, will be developed from detailed, experimentally validated, component models of the neuronal circuit, motoneuron activation, muscle biomechanics, and body-fluid interactions. Theoretical and computational studies of the model will then be performed to examine how the neural control circuits process sensory signals and achieve oscillations near a resonance under nominal and perturbed environmental conditions.

Once uncovered, resonance entrainment principles will contribute to understanding of general complex systems that achieve robustness, adaptability, and emergence. These new functionalities, when engineered, will have a broad range of applications, including robotic vehicles that maintain efficiency of transportation under varying environments. In the medical field, understanding of the neural control mechanisms is fundamental to determining the cause, rehabilitation, and cure for loss or reduction of locomotor ability due to neurological disorders or spinal cord injury. The research activities are integrated into educational activities at both K-12 and college levels. A graduate student will be trained for experimental data processing and model-based dynamical systems analyses. Through research experience activities, undergraduate students will develop educational software modules for simulation/animation of leech swimming to stimulate K-12 students' curiosity for science/engineering during such occasions as Engineering Open House. The results will be broadly disseminated to both biology and control engineering communities to enhance cross-cultural fertilization.

Biologically inspired feedback control of robots interacting with humans to cooperate and assist with repetitive movement tasks

The project will address the fundamental problem of how to control the motion of a robot so that it can cooperatively work with humans to assist them in repetitive tasks. Oscillatory body movements constitute an elementary means for various tasks in human living. Such repetitive movements include essential life functions such as heart beat, breathing, eating (chewing), walking; basic daily tasks such as brushing teeth, washing face; house-hold chores such as cleaning windows, sweeping floor; health/entertainment activities such as dancing, swimming, cycling, rowing; and manufacturing labors such as moving objects in factory assembly lines. Robots and mechanical devices that assist such human movements would be found useful in a number of contexts. A robotic manipulator and a human arm may grab a common tool to work together on repetitive tasks where the former assists the latter by providing force and stability to reduce burden on the human. An exoskeleton may be worn to complement reduced capability of, or provide rehabilitations for, elderly people and patients with neurological disorders or physical disabilities. Thus, well-designed assistive devices for oscillatory movements would significantly contribute to improving quality of human life. Design of robotic mechanisms for such assistive devices is surely a challenging task. Equally challenging is the design of control algorithms that command the actuators and govern the motion of the robotic device. The state-of-the-art control technologies allow a designer to program a robot to achieve prescribed motion with speed, precision, and robustness, as seen for instance in industrial manipulators. However, if such robots interact with humans, they would be perceived as stiff, stubborn, or even dangerous, and are therefore not suitable as co-robots in direct support of humans. What is needed is control algorithms that make robots understand human intentions, cooperate with humans without insisting on their preprogramed operations, and assist with human tasks. Development of such algorithms will be the focus of this project.

This basic research aims to establish a systematic method for designing a feedback controller for a general robotic system interacting with a human to stabilize the oscillation intended by the human and to reduce the burden on the human by providing assistive forces. The control architecture is inspired by the central pattern generator (CPG) -- neuronal circuits that command muscle contractions to achieve rhythmic body movements during animal locomotion. CPGs are attractive for engineering applications due to its ability to conform their oscillations to natural dynamics of a varying environment through sensory feedback. This exploratory research will investigate the potential of the CPG architecture to provide a viable foundation for a new system design for achieving co-robots that assist humans to execute oscillation tasks. The controller is realized as an interconnection of identical units, emulating neuronal dynamics. The problem is formulated as the search for the interconnection such that the robot-human-CPG system has a stable limit cycle in which human decides an appropriate oscillation and CPG-controlled robot assists. The method of multivariable harmonic balance will be employed to obtain a convex characterization of feasible interconnection matrices that meet oscillation specifications. The approximate nature of the design method will be complemented by extensive simulations as well as physical experiments on robotic manipulators. While the central theme of control theory has been the regulation around an equilibrium point of a dynamical system, capability of generating coordinated autonomous oscillations can be extremely useful in many engineering applications. The basic research proposed here will provide an initial stepping stone toward a new paradigm for cooperative pattern generations by feedback control.