Property:Abstract
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D
In this paper, we provide a theoretical framework that consists of
graph theoretical and Lyapunov-based approaches to stability analysis and
distributed control of multi-agent formations.
This framework relays on the
notion of graph rigidity as a means of identifying the shape variables
of a formation. Using this approach, we can
formally define formations of multiple vehicles and three types of
stabilization/tracking problems for dynamic multi-agent systems.
We show how these three problems can be addressed mutually independent of
each other for a formation of two agents. Then, we introduce a procedure
called dynamic node augmentation that allows construction of a larger
formation with more agents that can be rendered structurally stable
in a distributed manner from some initial formation that
is structurally stable. We provide two examples of formations
that can be controlled using this approach, namely, the V-formation
and the diamond formation. +
A
In this paper, we provide tools for convergence and
performance analysis of an agreement protocol for a
network of integrator agents with directed information
flow. We also analyze algorithmic robustness of this
consensus protocol for networks with mobile nodes and
switching topology. A connection is established between the Fiedler eigenvalue of the graph Laplacian and
the performance of this agreement protocol. We demon-
strate that a class of directed graphs, called balanced
graphs, have a crucial role in solving average-consensus
problems. Based on the properties of balanced graphs,
a group disagreement function (i.e. Lyapunov function)
is proposed for convergence analysis of this agreement
protocol for networks with directed graphs and switching topology. +
M
In this paper, we study automated test generation for discrete decision-making modules in autonomous systems. First, we consider a subset of Linear Temporal Logic to represent formal requirements on the system and the test environment. The system specification captures requirements for the system under test while the test specification captures basic attributes of the test environment known to the system, and additional structure provided by a test engineer, which is unknown to the system. Second, a game graph representing the high-level interaction between the system and the test environment is constructed from transition systems modeling the system and the test environment. We provide an algorithm that finds the projection of the acceptance conditions of the system and test specifications on the game graph. Finally, to ensure that the system meets the test specification in addition to satisfying the system specification, we present a framework to construct a minimally constrained test. Specifically, we formulate this as a multi-commodity network flows problem, and present two optimizations to solve for the minimally constrained test. We conclude with future directions on applying these algorithms to constrain test environments in self-driving applications. +
T
In this paper, we study the classical problem
of stabilizing a Linear Time Invariant (LTI) system in a
packet-based network setting. We assume that the LTI system
is unstable but both controllable and observable. The state
information is transmitted to the controller over a packetbased
network. We also assume that there is a perfect link
from the controller to the plant. We give a set of sufficient
conditions under which the system can be stabilized for a
given data rate C. In particular, these conditions can yield an
upper bound on the minimum C for which the system can
be stabilized. A recursive encoding-decoding scheme and an
associated control law are proposed to achieve stability for
rate exceeding this bound. An optimal bit allocation problem
is investigated in which we ask about how to allocate the
bits in a single packet for a subsystem of a general LTI
system such that a minimum upper bound on the data rate is
achieved.We then formulate the optimal bit allocation problem
as a Linear Matrix Inequality (LMI) optimization problem
which can be solved efficiently using standard Semi-definite
Programming (SDP) solvers. Examples and simulations are
given to demonstrate the results. +
C
In this paper, we synthesize a robust connected cruise controller with performance guarantee using probabilis- tic model checking, for a vehicle that receives motion informa- tion from several vehicles ahead through wireless vehicle-to- vehicle communication. We model the car-following dynamics of the preceding vehicles as Markov chains and synthesize the connected cruise controller as a Markov decision process. We show through simulations that such a design is robust against imperfections in communication. +
D
In this study, an Escherichia coli (E. coli) based transcription translation cell-free system (TX-TL) was employed to sample various enzyme expression levels of the violacein pathway. TX-TL enables rapid modifications and prototyping of the pathway without complicated cloning cycles. The violacein metabolic pathway has been successfully reconstructed in TX-TL. Analysis of the product via UV-Vis absorption and liquid chromatography-mass spectrometry detected 4.95 mM of violacein. Expression levels of pathway enzymes were modeled using the TX-TL Toolbox. The model revealed the length of an enzyme coding sequence (CDS) significantly affected its expression level. Finally, pathway exploration suggested an improvement in violacein production at high VioC and VioD DNA concentrations. +
E
In this work we consider a class of networked control systems (NCS) when the control signal is sent to the plant via a UDP-like communication protocol, the controller sends a communication packet to the plant across a lossy network but the controller..... +
A
An Estimation Algorithm for a Class of Networked Control Systems Using UDP-Like Communication Schemes +
In this work we consider a class of networked control
systems (NCS) when the control signal is sent to the plant
via a UDP-like communication protocol. In this case the
controller sends a communication packet to the plant across
a lossy network, but the controller does not receive any
acknowledgement signal indicating the status of the control
packet. Standard observer based estimators assume the
estimator has knowledge of what control signal is applied
to the plant. Under the UDP-like protocol the
controller/estimator does not have explicit knowledge
whether the control signals have been applied to the plant
or not. We present a simple estimation algorithm that
consists of a state and mode observer as well as a
constraint on the control signal sent to the plant. For
the class of systems considered, discrete time LTI plants
where at least one
of the states that is directly affected by the input is
also part of the measurement vector, the estimator is able
to recover the fate of the control packet from the
measurement at the next timestep and exhibit better
performance than other naive schemes. For
single-input-single-output (SISO) systems we are able to
show convergence properties of the estimation error and the
state. Simulations are provided to demonstrate the
algorithm and show it's effectiveness. +
C
Characterization of minimum inducer separation time for a two-input integrase-based event detector +
In this work, we present modeling and experimental characterization of the minimum time needed for flipping of a DNA substrate by a two-integrase event detector. The event detector logic diâµerentiates the temporal order of two chemical inducers. We find that bundling biological rate parameters (transcription, translation, DNA search- ing, DNA flipping) into only a few rate constants in a stochastic model is sufficient to accurately predict final DNA states. We show, through time course data in E.coli, that these modeling predictions are reproduced in vivo. We believe this model validation is critical for using integrase-based systems in larger circuits. +
L
Learning pose estimation for UAV autonomous navigation and landing using visual-inertial sensor data +
In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV's absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing. +
R
In vitro transcription and translation systems have been used to rapidly test and debug synthetic circuits, allowing for much faster design-build-test cycles. To demonstrate the power of in vitro prototyping, we designed 16 two-input logic gates using a library of 14 linear DNA constructs. We successfully implemented all 16 gates in an E. coli cell extract prototyping environment (TXTL), going from design to functionality in less than 3 months. In separate tests, each taking less than an hour to set up and less than 8 hours to run, we were able to quickly diagnose functionality of engineered parts, including quantification of promoter leakiness and promoter and repressor strength. In subsequent short tests we determined optimal circuit component ratios and investigated component interactions, including crosstalk and resource loading. To lower the entry barrier to in vitro testing, we also created a cellphone-based fluorescent imager that can be used to measure fluorescent output of paper-based TXTL reactions. We hope to establish in vitro testing as a rapid, easily accessible tool for engineering synthetic circuits in research and education. +
D
In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. One, P. synxantha, was further characterized. A lysate test of P. synxantha showed a maximum protein yield of 2.5 μM at 125 proteins per DNA template and a maximum protein synthesis rate of 20 nM/min. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL derived promoter strength and the in vivo promoter strength indicates these lysate-based cell-free systems can be used to characterize and engineer biological parts without genome integration, enabling a faster designbuild-test cycle. +
F
Failure-Tolerant Contract-Based Design of an Automated Valet Parking System using a Directive-Response Architecture +
Increased complexity in cyber-physical systems calls for modular system design methodologies that guarantee correct and reliable behavior, both in normal operations and in the presence of failures. This paper aims to extend the contract-based design approach using a directive-response architecture to enable reactivity to failure scenarios. The architecture is demonstrated on a modular automated valet parking (AVP) system. The contracts for the different components in the AVP system are explicitly defined, implemented, and validated against a Python implementation. +
B
Insects exhibit incredibly robust closed loop flight dynamics in the face of uncertainties.
A fundamental principle contributing to this unparalleled behavior is rapid processing and
convergence of visual sensory information to flight motor commands via spatial wide-field
integration, accomplished by retinal motion pattern sensitive interneurons (LPTCs) in the
lobula plate portion of the visual ganglia. Within a control-theoretic framework, an inner
product model for wide-field integration of retinal image flow is developed, representing the
spatial decompositions performed by LPTCs in the insect visuomotor system. A rigorous
characterization of the information available from this visuomotor convergence technique
for motion within environments exhibiting non-omogeneous spatial distributions is performed, establishing the connection between retinal motion sensitivity shape and closed
loop behavior. The proposed output feedback methodology is shown to be sufficient to give
rise to experimentally observed insect navigational heuristics, including forward speed regulation, obstacle avoidance, hovering, and terrain following behaviors. Hence, extraction of
global retinal motion cues through computationally efficient wide-field integration process-
ing provides a novel and promising methodology for utilizing visual sensory information in
autonomous robotic navigation and flight control applications. +
S
Insects exhibit unparalleled and incredibly robust flight dynamics in
the face of uncertainties. A fundamental principle contributing to this amazing
behavior is rapid processing and convergence of visual sensory information to flight
motor commands via spatial wide-field integration. Within the control-theoretic
framework presented here, a model for wide-field integration of retinal image flow
is developed which explains how various image flow kernels correspond to feedback
terms that stabilize the different modes of planar fiight. It is also demonstrated that
the proposed output feedback methodology is su±cient to explain experimentally
observed navigational heuristics as the centering and forward speed regulation
responses exhibited by honeybees. +
A
Insects exhibit unparalleled and incredibly robust
flight dynamics in the face of uncertainties. A fundamental principle contributing to this amazing behavior is rapid processing
and convergence of visual sensory information to flight motor
commands via spatial wide-field integration, accomplished by
motion pattern sensitive interneurons in the lobula plate portion
of the visual ganglia. Within a control-theoretic framework, a
model for wide-field integration of retinal image flow is developed, establishing the connection between image flow kernels
(retinal motion pattern sensitivities) and the feedback terms
they represent. It is demonstrated that the proposed output
feedback methodology is sufficient to give rise to experimentally
observed navigational heuristics as the centering and forward
speed regulation responses exhibited by honeybees. +
G
Integral control is commonly used in mechanical and electrical systems to ensure perfect adaptation. A proposed design of integral control for synthetic biological systems employs the sequestration of two biochemical controller species. The unbound amount of controller species captures the integral of the error between the current and the desired state of the system. However, implementing integral control inside bacterial cells using sequestration feedback has been challenging due to the controller molecules being degraded and diluted. Furthermore, integral control can only be achieved under stability conditions that not all sequestration feedback networks fulfill. In this work, we give guidelines for ensuring stability and good performance (small steady-state error) in sequestration feedback networks. Our guidelines provide simple tuning options to obtain a flexible and practical biological implementation of sequestration feedback control. Using tools and metrics from control theory, we pave the path for the systematic design of synthetic biological systems. +
A
A 65nm CMOS Living-Cell Dynamic Fluorescence Sensor with 1.05fA Sensitivity at 600/700nm Wavelengths +
Integrated, low-cost and miniaturized devices that can detect clinically relevant biomarkers are crucial for the growing field of precision medicine as they can enable point-of-care diagnosis, continuous health monitoring and closed-loop drug delivery. Fluorescence (FL) sensing is known to be one of the most reliable, sensitive, and widely adopted sensing modality for many biomarkers. However, detecting the weak FL signal requires complex optical setups, especially narrowband optical filters to block the strong excitation (EX) light. Prior efforts to miniaturize and implement FL sensing in CMOS technologies have been limited to on-chip high-pass filters using dense vertical waveguide arrays. More importantly, the reported wavelength range of 800nm in prior work is not compatible with most of the commonly used fluorescent proteins that work with living cells. Luminescence is another mechanism for detecting biomarkers that does not require an EX source and optical filtering. However, there are limited number of luminescence proteins, and it is not feasible to use them in a closed-loop system since they can interfere with optogenetic control integration. +
S
Intercommunication of the microbiome-gut-brain axis occurs through various signaling pathways including the vagus nerve, immune system, endocrine/paracrine, and bacteria-derived metabolites. But how these pathways integrate to influence cognition remains undefined. In this paper, we create a systems level mathematical framework comprised of interconnected organ-level dynamical subsystems to increase conceptual understanding of how these subsystems contribute to cognitive performance. With this framework we propose that control of hippocampal long-term potentiation (hypothesized to correlate with cognitive performance) is influenced by inter- organ signaling with diet as the external control input. Specifically, diet can influence synaptic strength (LTP) homeostatic conditions necessary for learning. The proposed model provides new qualitative information about the functional relationship between diet and output cognitive performance. The results can give insight for optimization of cognitive performance via diet in experimental animal models. +
A
It has been shown that optimal controller synthesis for positive systems can be formulated as a linear program. Leveraging these results, we propose a scalable iterative algo- rithm for the systematic design of sparse, small gain feedback strategies that stabilize the evolutionary dynamics of a generic disease model. We achieve the desired feedback structure by augmenting the optimization problems with `1 and `2 regular- ization terms, and illustrate our method on an example inspired by an experimental study aimed at finding appropriate HIV neutralizing antibody therapy combinations in the presence of escape mutants. +
