Property:Abstract
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A
Experimental comparisons between four different control design
methodologies are applied to a small vectored thrust engine.
Each controller is applied to three trajectories of varying
aggressiveness. The control strategies considered are LQR,
H_infty, gain scheduling, and feedback linearization. The
experiments show that gain scheduling
is essential to achieving good performance. The
strengths and weaknesses of each methodology are also examined. +
M
Experiments using active control to reduce oscillations
in the flow past a rectangular cavity have uncovered
surprising phenomena: in the controlled system,
often new frequencies of oscillation appear, and
often the main frequency of oscillation is split into
two sideband frequencies. The goal of this paper is
to explain these effects using physics-based models,
and to use these ideas to guide control design.
We present a linear model for the cavity flow,
based on the physical mechanisms of the familiar
Rossiter model. Experimental data indicates
that under many operating conditions, the oscillations
are not self-sustained, but in fact are caused
by amplification of external disturbances. We
present some experimental results demonstrating
the peak-splitting phenomena mentioned above, use
the physics-based model to study the phenomena,
and discuss fundamental performance limitations
which limit the achievable performance of any control
scheme. +
P
Planning and Optimization for Multi-Robot Planetary Cave Exploration under Intermittent Connectivity Constraints +
Exploring subsurface structures with autonomous robots is of growing interest in the context of planetary caves studies. Communication between robots in these environments is severely degraded which complicates coordination and information distribution. In this paper we focus on planning for mobility and communication in a cave exploration scenario where the situational awareness of a static base station is critical. We propose a notion of information-consistency where a plan itself is part of the information to be shared between robots, and propose a method for generating informationconsistent plans. We discuss in detail how the resulting plan can be robustly implemented with minimal communication through local mission executives that run on individual robots. We describe preliminary results on the performance of the planning algorithm and integration of the local mission executives in a high-fidelity simulation environment. +
F
Fault tolerance and safety verification of control systems that have state estimation uncertainty are essential for the success of autonomous robotic systems. A software control architecture called Mission Data System, developed at the Jet Propulsion Laboratory, uses goal networks as the control program for autonomous systems. Certain types of goal networks can be converted into linear hybrid systems and verified for safety using existing symbolic model checking software. A process for calculating the probability of failure of some verifiable goal networks due to state estimation uncertainty is presented. Extensions of this procedure to include other types of uncertainties are discussed, and example problems are presented to illustrate these procedures. +
C
Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System (MDS), developed at the Jet Propulsion Laboratory, takes a goal-based control approach. A software algorithm for converting goal network control programs into linear hybrid systems exists and is a bisimulation; the resulting linear hybrid system can be verified for safety in the presence of failures using existing symbolic model checkers, and thus the original goal network is verified. A substantial example control program based on a proposed mission to Titan, a moon of Saturn, is converted using the procedures discussed. +
A
Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System (MDS), developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a software algorithm for converting goal network control programs into linear hybrid systems is described. The conversion process is a bisimulation; the resulting linear hybrid system can be verified for safety in the presence of failures using existing symbolic model checkers, and thus the original goal network is verified. A moderately complex goal network control program is converted to a linear hybrid system using the automatic conversion software and then verified. +
C
Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System, developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a method for converting goal network control programs into linear hybrid systems is developed. The linear hybrid system can then be verified for safety in the presence of failures using existing symbolic model checkers. An example task is developed and successfully verified using HyTech, a symbolic model checking software for linear hybrid systems. +
S
Fault tolerance and safety verification of control systems that have state variable estimation uncertainty are essential for the success of autonomous robotic systems. A software control architecture called Mission Data System, developed at the Jet Propulsion Laboratory, uses goal networks as the control program for autonomous systems. Certain types of goal networks can be converted into linear hybrid systems and verified for safety using existing symbolic model checking software. A process for calculating the probability of failure of certain classes of verifiable goal networks due to state estimation uncertainty is presented. A verifiable example task is presented and the failure probability of the control program based on
estimation uncertainty is found. +
D
Feedback control is the key to achieve robust performances for many engineered systems. However, its application in biological contexts is still largely unexplored. In this work, we designed, analyzed and simulated a layered controller functioning at both molecular and populational levels. First, we used a minimal model of three states to represent a system where state A activates state B; state R is a by-product of state B that acts as a negative feedback regulating both state A, B, and sequentially R. We call the feedback applied to state B a cis feedback and the one applied to state A a trans feedback. Through stability analysis via linearization at equilibrium and sensitivity analysis at transient state, we found that the cis feedback attenuates disturbances better but recovers slower; the trans feedback recovers faster but has more dramatic responses to fluctuations; the layered feedback demonstrates both advantageous traits of the two single layers. Then we designed two versions of synthetic genetic circuits to implement the layered controller in living cells. One version with an sRNA as regulator R, the other with a transcription factor protein as the regulator R. The analysis and dynamical simulation of the models confirmed the analytical results from the minimal model. At the same time, we found that the protein regulated feedback controls have faster recovery speed but the RNA version has a stronger disturbance attenuation effect. +
S
Synergistic dual positive feedback loops established by molecular sequestration generate robust bimodal response +
Feedback loops are ubiquitous features of biological networks and can produce significant phenotypic heterogeneity, including a bimodal distribution of gene expression across an isogenic cell population. In this work, a combination of experiments and computational modeling was used to explore the roles of multiple feedback loops in the bimodal, switch-like response of the Saccharomyces cerevisiae galactose regulatory network. Here, we show that bistability underlies the observed bimodality, as opposed to stochastic effects, and that two unique positive feedback loops established by Gal1p and Gal3p, which both regulate network activity by molecular sequestration of Gal80p, induce this bimodality. Indeed, systematically scanning through different single and multiple feedback loop knockouts, we demonstrate that there is always a concentration regime that preserves the systemâs bimodality, except for the double deletion of GAL1 and the GAL3 feedback loop, which exhibits a graded response for all conditions tested. The constitutive production rates of Gal1p and Gal3p operate as bifurcation parameters because variations in these rates can also abolish the systemâs bimodal response. Our model indicates that this second loss of bistability ensues from the inactivation of the remaining feedback loop by the overexpressed regulatory component. More broadly, we show that the sequestration binding affinity is a critical parameter that can tune the range of conditions for bistability in a circuit with positive feedback established by molecular sequestration. In this system, two positive feedback loops can significantly enhance the region of bistability and the dynamic response time. +
H
Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism implemented using the sequestration binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of sequestration feedback and contribute to a more general theory of biological control systems. +
F
Flat systems, an important subclass of nonlinear control systems introduced
via differential-algebraic methods, are defined in a differential
geometric framework. We utilize the infinite dimensional geometry developed
by Vinogradov and coworkers: a control system is a diffiety, or more
precisely, an ordinary diffiety, i.e. a smooth infinite-dimensional manifold
equipped with a privileged vector field. After recalling the definition of
a Lie-Backlund mapping, we say that two systems are equivalent if they
are related by a Lie-Backlund isomorphism. Flat systems are those systems
which are equivalent to a controllable linear one. The interest of
such an abstract setting relies mainly on the fact that the above system
equivalence is interpreted in terms of endogenous dynamic feedback. The
presentation is as elementary as possible and illustrated by the VTOL
aircraft. +
T
For closed-loop control of thin film deposition, one would like to
directly control film properties such as roughness, stress, or
composition, rather than process parameters like
temperatures and flow rates. This requires a
model of the dynamic response of film properties to
changes in process conditions.
Direct atomistic simulation is far too slow to be used in this
capacity, but a promising approach we explore here is to derive
reduced-order dynamic models from atomistic simulations.
<p>
In this paper, we consider film growth on a vicinal surface
using a kinetic Monte
Carlo model. The temperature range spans the transition from
smooth step flow to rough island growth.
Proper Orthogonal Decomposition is used to extract
the dominant spatial modes from the KMC simulations. Only five spatial modes
adequately represent the roughness dynamics for all simulated times and
temperatures, indicating that a 5-state model may be
sufficient for real-time roughness control. +
S
For state estimation in networked control systems,
the impact of packet dropping and delay over network links is
an important problem. In this paper, we introduce multiple description
(MD) source coding scheme to improve the statistical
stability and performance of the estimation error covariance
of Kalman filtering with packet loss. We consider about two
cases: when the packet loss over network links occurs in an i.i.d.
fashion or in a bursty fashion. Compared with the traditional
single description source coding, MD coding scheme can greatly
improve the performance of Kalman filtering over a large set
of packet loss scenarios in both cases. +
For state estimation over a communication network, efficiency and reliability of the network are critical issues. The presence
of packet dropping and communication delay can greatly impair our ability to measure and predict states. In this paper,
multiple description (MD) codes, a type of network source codes, are used to compensate for this effect on Kalman filtering.
We consider two packet dropping models: in one model, packet dropping occurs according to an independent and identically
distributed (i.i.d.) Bernoulli random process and in the other model, packet dropping is bursty and occurs according to a
Markov chain. We show that MD codes greatly improve the statistical stability and performance of Kalman filter over a large
set of packet loss scenarios in both cases. Our conclusions are verified by simulation results. +
P
For the synthesis of correct-by-construction control policies from temporal logic specifications the scalability of the synthesis algorithms is often a bottleneck. In this paper, we parallelize synthesis from specifications in the GR(1) fragment of linear temporal logic by introducing a hierarchical procedure that allows decoupling of the fixpoint computations. The state space is partitioned into equicontrollable sets using solutions to parameterized reachability games that arise from decomposing the original GR(1) game into smaller reachability games. Following the partitioning, another synthesis problem is formulated for composing the strategies from the decomposed reachability games. The formulation guarantees that composing the synthesized controllers ensures satisfaction of the given GR(1) property. Benchmarking experiments with robot planning problems demonstrate good scalability of the approach. +
A
Gene expression is often controlled by natural genetic regulatory networks that govern the rates at which genes
are transcribed. Recent work has shown that synthetic versions of genetic networks can be designed and built in living cells. Applications for these synthetic regulatory networks include intracellular decision-making and computation. In this study,
we propose a new synthetic genetic network that behaves as a digital clock, producing square waveform oscillations. We analyze two models of the network, a deterministic model based on Michaelis-Menten kinetics, as well as a stochastic model based on the Gillespie algorithm. Both models predict regions of oscillatory behavior; the deterministic model provides insight into the conditions required to produce the oscillating clock-like behavior, while the stochastic model is truer to natural
dynamics. Intracellular stochasticity is seen to contribute phase noise to the oscillator, and we propose improvements for the network and discuss the conceptual foundations of these improvements. +
R
Gene regulatory interactions are context dependent, active in some cellular states but not in others. Stochastic fluctuations, or 'noise', in gene expression propagate through active, but not inactive, regulatory links. Thus, correlations in gene expression noise could provide a noninvasive means to probe the activity states of regulatory links. However, global, 'extrinsic', noise sources generate correlations even without direct regulatory links. Here we show that single-cell time-lapse microscopy, by revealing time lags due to regulation, can discriminate between active regulatory connections and extrinsic noise. We demonstrate this principle mathematically, using stochastic modeling, and experimentally, using simple synthetic gene circuits. We then use this approach to analyze dynamic noise correlations in the galactose metabolism genes of Escherichia coli. We find that the CRP-GalS-GalE feed-forward loop is inactive in standard conditions but can become active in a GalR mutant. These results show how noise can help analyze the context dependence of regulatory interactions in endogenous gene circuits. +
D
Genetic regulatory networks are biochemical reaction systems, consisting of a network of interacting genes and associated proteins. The dynamics of genetic regulatory networks contain many complex facets that require careful consideration during the modeling process. The classical modeling approach involves studying systems of ordinary differential equations (ODEs) that model biochemical reactions in a deterministic, continuous, and instantaneous fashion. In reality, the dynamics of these systems are stochastic, discrete, and widely delayed. The first two complications are often successfully addressed by modeling regulatory networks using the Gillespie stochastic simulation algorithm (SSA), while the delayed behavior of biochemical events such as transcription and translation are often ignored due to their mathematically difficult nature. We develop techniques based on delay-differential equations (DDEs) and the delayed Gillespie SSA to study the effects of delays, in both continuous deterministic and discrete stochastic settings. Our analysis applies techniques from Floquet theory and advanced numerical analysis within the context of delay-differential equations, and we are able to derive stability sensitivities for biochemical switches and oscillators across the constituent pathways, showing which pathways in the regulatory networks improve or worsen the stability of the system attractors. These delay sensitivities can be far from trivial, and we offer a computational framework validated across multiple levels of modeling fidelity. This work suggests that delays may play an important and previously overlooked role in providing robust dynamical behavior for certain genetic regulatory networks, and perhaps more importantly, may offer an accessible tuning parameter for robust bioengineering. +
Given a differentially flat system of ODEs, flat outputs that depend only on original
variables but not on their derivatives are called zero-flat outputs and systems possessing
such outputs are called zero-flat. In this paper we present a theory of zero-flatness for
a system of two one-forms in arbitrary number of variables $(t,x^1,\dots,x^N)$. Our
approach splits the task of finding zero-flat outputs into two parts. First part involves
solving for distributions that satisfy a set of algebraic conditions. If the first part
has no solution then the system is not zero-flat. The second part involves finding an
integrable distribution from the solution set of the first part. Typically this part
involves solving PDEs. Our results are also applicable in determining if a control affine
system in $n$ states and $n-2$ controls has flat outputs that depend only on states. We
illustrate our method by examples. +
