Journal updates for 2015.05.31 Biophysical Journal Vol. 108, no. 9 Anomalous Extracellular Diffusion in Rat Cerebellum Fanrong Xiao, Jan Hrabe, Sabina Hrabetova Department of Cell Biology, State University of New York, Downstate Medical Center, Brooklyn, New York Medical Physics Laboratory, Nathan S. Kline Institute, Orangeburg, New York The Robert F. Furchgott Center for Neural and Behavioral Science, State University of New York Downstate Medical Center, Brooklyn, New York Extracellular space (ECS) is a major channel transporting biologically active molecules and drugs in the brain. Diffusion-mediated transport of these substances is hindered by the ECS structure but the microscopic basis of this hindrance is not fully understood. One hypothesis proposes that the hindrance originates in large part from the presence of dead-space (DS) microdomains that can transiently retain diffusing molecules. Because previous theoretical and modeling work reported an initial period of anomalous diffusion in similar environments, we expected that brain regions densely populated by DS microdomains would exhibit anomalous extracellular diffusion. Specifically, we targeted granular layers (GL) of rat and turtle cerebella that are populated with large and geometrically complex glomeruli. The integrative optical imaging (IOI) method was employed to evaluate diffusion of fluorophore-labeled dextran (MW 3000) in GL, and the IOI data analysis was adapted to quantify the anomalous diffusion exponent dw from the IOI records. Diffusion was significantly anomalous in rat GL, where dw reached 4.8. In the geometrically simpler turtle GL, dw was elevated but not robustly anomalous (dw = 2.6). The experimental work was complemented by numerical Monte Carlo simulations of anomalous ECS diffusion in several three-dimensional tissue models containing glomeruli-like structures. It demonstrated that both the duration of transiently anomalous diffusion and the anomalous exponent depend on the size of model glomeruli and the degree of their wrapping. In conclusion, we have found anomalous extracellular diffusion in the GL of rat cerebellum. This finding lends support to the DS microdomain hypothesis. Transiently anomalous diffusion also has a profound effect on the spatiotemporal distribution of molecules released into the ECS, especially at diffusion distances on the order of a few cell diameters, speeding up short-range diffusion-mediated signals in less permeable structures. %=============================================================

Proceedings of the National Academy of Sciences, USA

(vol. 112, no. 18) Nothing of interest

(vol. 112, no. 19) Modulation of orthogonal body waves enables high maneuverability in sidewinding locomotion

1. Henry C. Astley o aGeorgia Institute of Technology, Atlanta, GA 30332;

2. Chaohui Gong o bCarnegie Mellon University, Pittsburgh, PA 15213; and

3. Jin Dai o bCarnegie Mellon University, Pittsburgh, PA 15213; and

4. Matthew Travers o bCarnegie Mellon University, Pittsburgh, PA 15213; and

5. Miguel M. Serrano o aGeorgia Institute of Technology, Atlanta, GA 30332;

6. Patricio A. Vela o aGeorgia Institute of Technology, Atlanta, GA 30332;

7. Howie Choset o bCarnegie Mellon University, Pittsburgh, PA 15213; and

8. Joseph R. Mendelson, III o aGeorgia Institute of Technology, Atlanta, GA 30332; o cZoo Atlanta, Atlanta, GA 30315

9. David L. Hu o aGeorgia Institute of Technology, Atlanta, GA 30332;

10. Daniel I. Goldman o aGeorgia Institute of Technology, Atlanta, GA 30332;

Many organisms move using traveling waves of body undulation, and most work has focused on single-plane undulations in fluids. Less attention has been paid to multiplane undulations, which are particularly important in terrestrial environments where vertical undulations can regulate substrate contact. A seemingly complex mode of snake locomotion, sidewinding, can be described by the superposition of two waves: horizontal and vertical body waves with a phase difference of ±90°. We demonstrate that the high maneuverability displayed by sidewinder rattlesnakes (Crotalus cerastes) emerges from the animal’s ability to independently modulate these waves. Sidewinder rattlesnakes used two distinct turning methods, which we term differential turning (26° change in orientation per wave cycle) and reversal turning (89°). Observations of the snakes suggested that during differential turning the animals imposed an amplitude modulation in the horizontal wave whereas in reversal turning they shifted the phase of the vertical wave by 180°. We tested these mechanisms using a multimodule snake robot as a physical model, successfully generating differential and reversal turning with performance comparable to that of the organisms. Further manipulations of the two-wave system revealed a third turning mode, frequency turning, not observed in biological snakes, which produced large (127°) in-place turns. The two-wave system thus functions as a template (a targeted motor pattern) that enables complex behaviors in a high-degree-of-freedom system to emerge from relatively simple modulations to a basic pattern. Our study reveals the utility of templates in understanding the control of biological movement as well as in developing control schemes for limbless robots.

(vol. 112, no. 20,21) Nothing of interest.

%=============================================================

Review of Scientific Instruments

(vol. 86, no. 5)

Not yet out. %=============================================================

IEEE Transactions on Automatic Control (vol. 60, no. 5) Nothing of interest (vol. 60, no. 6) Distributed Controllers for Multi-Agent Coordination Via Gradient-Flow Approach K. Sakurama, S. Azuma, and T. Sugie Tottori University, Kyoto University This paper provides a unified solution for a general distributed control problem of multi-agent systems based on the gradient-flow approach. First, a generalized coordination is presented as a control objective which represents a wide range of coordination tasks (e.g., consensus, formation and pattern decision) in a unified manner. Second, a necessary and sufficient condition for the gradient-based controllers to be distributed is derived. It turns out that the notion of clique (i.e., complete subgraph) plays a crucial role to obtain any distributed controllers. Furthermore, all such controllers are explicitly characterized with free design parameters. Third, it is shown how to choose an optimal controller in a systematic way among all distributed ones, where an optimality measure is introduced for the generalized coordination. Finally, the effectiveness of the proposed method is demonstrated through simulations, where a distributed pattern decision is discussed as an example of the generalized coordination. An Optimal Control Approach to the Multi-Agent Persistent Monitoring Problem in Two-Dimensional Spaces X. Lin and C. Cassandras Boston University We address the persistent monitoring problem in two-dimensional mission spaces where the objective is to control the trajectories of multiple cooperating agents to minimize an uncertainty metric. In a one-dimensional mission space, we have shown that the optimal solution is for each agent to move at maximal speed and switch direction at specific points, possibly waiting some time at each such point before switching. In a two-dimensional mission space, such simple solutions can no longer be derived. An alternative is to optimally assign each agent a linear trajectory, motivated by the one-dimensional analysis. We prove, however, that elliptical trajectories outperform linear ones. With this motivation, we formulate a parametric optimization problem in which we seek to determine such trajectories. We show that the problem can be solved using Infinitesimal Perturbation Analysis (IPA) to obtain performance gradients on line and obtain a complete and scalable solution. Since the solutions obtained are generally locally optimal, we incorporate a stochastic comparison algorithm for deriving globally optimal elliptical trajectories. Numerical examples are included to illustrate the main result, allow for uncertainties modeled as stochastic processes, and compare our proposed scalable approach to trajectories obtained through off-line computationally intensive solutions.

%=============================================================

+ one:

SIAM Journal on Imaging Science

Vol. 1, no. 1, pp. 143-148

Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing Wotao Yin, Stanley Osher, Donald Goldfarb, and Jerome Darbon We propose simple and extremely efficient methods for solving the basis pursuit problem $\min\{\|u\|_1 : Au = f, u\in\mathbb{R}^n\},$ which is used in compressed sensing. Our methods are based on Bregman iterative regularization, and they give a very accurate solution after solving only a very small number of instances of the unconstrained problem $\min_{u\in\mathbb{R}^n} \mu\|u\|_1+\frac{1}{2}\|Au-f^k\|_2^2$ for given matrix A and vector $f^k$. We show analytically that this iterative approach yields exact solutions in a finite number of steps and present numerical results that demonstrate that as few as two to six iterations are sufficient in most cases. Our approach is especially useful for many compressed sensing applications where matrix-vector operations involving A and $A^\top$ can be computed by fast transforms. Utilizing a fast fixed-point continuation solver that is based solely on such operations for solving the above unconstrained subproblem, we were able to quickly solve huge instances of compressed sensing problems on a standard PC.

IEEE Transactions on Signal Processing (Issue 11, 12, 2015)

IEEE Transactions on Image Processing (Issue 8, 2015)

Simultaneous Multibeam Resource Allocation Scheme for Multiple Target TrackingYan, J. ; Liu, H. ; Jiu, B. ; Chen, B. ; Liu, Z. ; Bao, Z.Xidian University, China

AbstractA colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam in-formation. However, the simultaneous multibeam working mode has two finite working resources: the numberof beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategyfor the multibeam working mode with the task of tracking multiple targets is developed in this paper. Thebasis of our technique is to adjust the number of beams and their directions and the transmit power of eachbeam through feedback, with the purpose of improving the worst tracking performance among the multipletargets. The Bayesian CramrRao lower bound (BCRLB) provides us with a lower bound on the estimatedmean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion forthe resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex butcan be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, whichgreatly aids real-time resource management. Numerical results show that the worst case tracking accuracy canbe efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.————————

l0 Sparse Inverse Covariance EstimationMarjanovic, G., Hero, A.O.School of Electrical Engineering, University of New South Wales, Sydney, Australia

AbstractRecently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance(precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convexl1 norm. However, the best estimator performance is not always achieved with this penalty. The most naturalsparsity promoting norm is the nonconvex l0 penalty but its lack of convexity has deterred its use in sparse max-imum likelihood estimation. In this paper, we consider nonconvex l0 penalized log-likelihood inverse covarianceestimation and present a novel cyclic descent algorithm for its optimization. Convergence to a local minimizeris proved, which is highly nontrivial, and we demonstrate via simulations the reduced bias and superior qualityof the l0 penalty as compared to the l1 penalty.————————

Model Order Selection Based on Information Theoretic Criteria: Design of the PenaltyMariani, A. ; Giorgetti, A. ; Chiani, M.DEI, Univ. of Bologna, Cesena, Italy

AbstractInformation theoretic criteria (ITC) have been widely adopted in engineering and statistics for selecting amongan ordered set of candidate models the one that better fits the observed sample data. The selected modelminimizes a penalized likelihood metric, where the penalty is determined by the criterion adopted. While rules

1

for choosing a penalty that guarantees a consistent estimate of the model order are known, theoretical tools forits design with finite samples have never been provided in a general setting. In this paper, we study model orderselection for finite samples under a design perspective, focusing on the generalized information criterion (GIC),which embraces the most common ITC. The theory is general, and as case studies we consider: a) the problemof estimating the number of signals embedded in additive white Gaussian noise (AWGN) by using multiplesensors; b) model selection for the general linear model (GLM), which includes, e.g., the problem of estimatingthe number of sinusoids in AWGN. The analysis reveals a trade-off between the probabilities of overestimatingand underestimating the order of the model. We then propose to design the GIC penalty to minimize underes-timation while keeping the overestimation probability below a specified level. For the considered problems thismethod leads to analytical derivation of the optimal penalty for a given sample size. A performance comparisonbetween the penalty optimized GIC and common AIC and BIC is provided, demonstrating the effectiveness ofthe proposed design strategy.————————

Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed SensingYu Wang, Jinshan Zeng, Zhimin Peng, Xiangyu Chang, and Zongben XuSch. of Math. Stat., Xi’an Jiaotong Univ., Xi’an, China

AbstractThis paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressedsensing. We first introduce a generalized restricted isometry property (gRIP). Then, we prove that the AITalgorithm converges to the original sparse solution at a linear rate under a certain gRIP condition in the noisefree case. While in the noisy case, its convergence rate is also linear until attaining a certain error bound.Moreover, as by-products, we also provide some sufficient conditions for the convergence of the AIT algorithmbased on the two well-known properties, i.e., the coherence property and the restricted isometry property (RIP),respectively. It should be pointed out that such two properties are special cases of gRIP. The solid improve-ments on the theoretical results are demonstrated and compared with the known results. Finally, we provide aseries of simulations to verify the correctness of the theoretical assertions as well as the effectiveness of the AITalgorithm.————————

Approximation and Compression With Sparse Orthonormal TransformsSezer, O.G. ; Guleryuz, O.G. ; Altunbasak, Y.Mobile Processor Innovation Lab., Samsung Mobile, Richardson, TX, USA

AbstractWe propose a new transform design method that targets the generation of compression-optimized transformsfor next-generation multimedia applications. The fundamental idea behind transform compression is to exploitregularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, inparticular images and video, are well known to contain a diverse set of localized structures, leading to manydifferent types of regularity and to nonstationary signal statistics. The proposed method designs sparse or-thonormal transforms (SOTs) that automatically exploit regularity over different signal structures and providesan adaptation method that determines the best representation over localized regions. Unlike earlier work thatis motivated by linear approximation constructs and model-based designs that are limited to specific types ofsignal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantlybroaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loevetransform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussianstatistics to significantly improve over the KLT on more general processes. We provide an algebraic optimizationframework that generates optimized designs for any desired transform structure (multiresolution, block, lapped,and so on) with significantly better n-term approximation performance. For each structure, we propose a newprototype codec and test over a database of images. Simulation results show consistent increase in compressionand approximation performance compared with conventional methods.————————

2

Background Subtraction Based on Low-Rank and Structured Sparse DecompositionXin Liu; Guoying Zhao; Jiawen Yao; Chun QiSch of Electron Inf Eng, Xi’an Jiaotong Univ.

AbstractLow rank and sparse representation based methods, which make few specific assumptions about the background,have recently attracted wide attention in background modeling. With these methods, moving objects in thescene are modeled as pixel-wised sparse outliers. However, in many practical scenarios, the distributions ofthese moving parts are not truly pixel-wised sparse but structurally sparse. Meanwhile a robust analysis mech-anism is required to handle background regions or foreground movements with varying scales. Based on thesetwo observations, we first introduce a class of structured sparsity-inducing norms to model moving objects invideos. In our approach, we regard the observed sequence as being constituted of two terms, a low-rank matrix(background) and a structured sparse outlier matrix (foreground). Next, in virtue of adaptive parameters fordynamic videos, we propose a saliency measurement to dynamically estimate the support of the foreground.Experiments on challenging well known data sets demonstrate that the proposed approach outperforms thestate-of-the-art methods and works effectively on a wide range of complex videos.————————

3

Optics Express

Volume 23, Issue 11

High-resolution, label-free imaging of living cells with direct electron-beam-excitation-assisted optical microscopyYasunori Nawa, Wataru Inami, Sheng Lin, Yoshimasa Kawata, and Susumu TerakawaShizuoka University, Japan

High spatial resolution microscope is desired for deep understanding of cellular functions, in order to develop medical technologies.We demonstrate high-resolution imaging of un-labelled organelles in living cells, in which live cells on a 50 nm thick silicon nitridemembrane are imaged by autofluorescence excited with a focused electron beam through the membrane. Electron beam excitationenables ultrahigh spatial resolution imaging of organelles, such as mitochondria, nuclei, and various granules. Since the autofluores-cence spectra represent molecular species, this microscopy allows fast and detailed investigations of cellular status in living cells.

Volume 23, Issue 10

Nothing of interest.

Physical Review E

Volume 91, Issue 5

Signatures of infinity: Nonergodicity and resource scaling in prediction, complexity, and learningJames P. Crutchfield and Sarah MarzenUniv. California Davis and Berkeley, USA

We introduce a simple analysis of the structural complexity of infinite-memory processes built from random samples of station-ary, ergodic finite-memory component processes. Such processes are familiar from the well known multiarm Bandit problem. Wecontrast our analysis with computation-theoretic and statistical inference approaches to understanding their complexity. The re-sult is an alternative view of the relationship between predictability, complexity, and learning that highlights the distinct ways inwhich informational and correlational divergences arise in complex ergodic and nonergodic processes. We draw out consequences forthe resource divergences that delineate the structural hierarchy of ergodic processes and for processes that are themselves hierarchical.

Convex hull of a Brownian motion in confinementMarie Chupeau, Olivier Benichou, and Satya N. MajumdarPierre and Marie Curie University, France

We study the effect of confinement on the mean perimeter of the convex hull of a planar Brownian motion, defined as the minimumconvex polygon enclosing the trajectory. We use a minimal model where an infinite reflecting wall confines the walk to one side.We show that the mean perimeter displays a surprising minimum with respect to the starting distance to the wall and exhibitsa nonanalyticity for small distances. In addition, the mean span of the trajectory in a fixed direction θ ∈ [0, π/2], which can beshown to yield the mean perimeter by integration over θ, presents these same two characteristics. This is in striking contrast to theone-dimensional case, where the mean span is an increasing analytical function. The nonmonotonicity in the two-dimensional caseoriginates from the competition between two antagonistic effects due to the presence of the wall: reduction of the space accessible tothe Brownian motion and effective repulsion.

Anomalous diffusion in stochastic systems with nonhomogeneously distributed trapsTomasz SrokowskiPolish Academy of Sciences, Poland

The stochastic motion in a nonhomogeneous medium with traps is studied and diffusion properties of that system are discussed.The particle is subjected to a stochastic stimulation obeying a general Levy stable statistics and experiences long rests due to non-homogeneously distributed traps. The memory is taken into account by subordination of that process to a random time; then thesubordination equation is position dependent. The problem is approximated by a decoupling of the medium structure and memoryand exactly solved for a power-law position dependence of the memory. In the case of the Gaussian statistics, the density distribu-tion and moments are derived: depending on geometry and memory parameters, the system may reveal both the subdiffusion andenhanced diffusion. The similar analysis is performed for the Levy flights where the finiteness of the variance follows from a variablenoise intensity near a boundary. Two diffusion regimes are found: in the bulk and near the surface. The anomalous diffusion exponentas a function of the system parameters is derived.

From physical linear systems to discrete-time series. A guide for analysis of the sampled experimental data

1

Jakub Slezak and Aleksander WeronWroclaw University of Technology, Poland

Modeling physical data with linear discrete-time series, namely, the autoregressive fractionally integrated moving average (ARFIMA)model, is a technique that has attracted attention in recent years. However, this model is used mainly as a statistical tool only, withweak emphasis on the physical background of the model. The main reason for this lack of attention is that the ARFIMA model de-scribes discrete-time measurements, whereas physical models are formulated using continuous-time parameters. In order to eliminatethis discrepancy, we show that time series of this type can be regarded as sampled trajectories of the coordinates governed by a systemof linear stochastic differential equations with constant coefficients. The observed correspondence provides formulas linking ARFIMAparameters and the coefficients of the underlying physical stochastic system, thus providing a bridge between continuous-time lineardynamical systems and ARFIMA models.

Currently Unpublished

Particle ancestor sampling for near-degenerate or intractable state transition modelsFredrik Lindsten, Pete Bunch, Sumeetpal S. Singh, Thomas B. Sch onUppsala University, Sweden

We consider Bayesian inference in sequential latent variable models in general, and in nonlinear state space models in particular(i.e., state smoothing). We work with sequential Monte Carlo (SMC) algorithms, which provide a powerful inference framework foraddressing this problem. However, for certain challenging and common model classes the state-of-the-art algorithms still struggle.The work is motivated in particular by two such model classes: (i) models where the state transition kernel is (nearly) degenerate,i.e. (nearly) concentrated on a low-dimensional manifold, and (ii) models where point-wise evaluation of the state transition densityis intractable. Both types of models arise in many applications of interest, including tracking, epidemiology, and econometrics.The difficulties with these types of models is that they essentially rule out forward-backward-based methods, which are known tobe of great practical importance, not least to construct computationally efficient particle Markov chain Monte Carlo (PMCMC)algorithms. To alleviate this, we propose a “particle rejuvenation” technique to enable the use of the forward-backward strategy for(nearly) degenerate models and, by extension, for intractable models. We derive the proposed method specifically within the contextof PMCMC, but we emphasise that it is applicable to any forward-backward-based Monte Carlo method.

2

Mechatronics

No issue released for May

Plus One Control Engineering Practice (Volume 41, August 2015) Backstepping-based robust-adaptive control of a nonlinear 2-DOF piezoactuator Pages 57–71

Juan-Antonio Escarenoa, Micky Rakotondrabeb, , , Didace Habinezab a Polytechnic Institute of Advanced Sciences, 7-9 rue M. Grandcoing, 94200

Ivry-sur-Seine, France b Automatic Control and Micro-Mechatronic Systems depart. (AS2M), FEMTO-ST

Institute, CNRS - University of Franche-Comté at Besançon (UFC) - ENSMM - UTBM,

Besançon, France

Abstract

This paper deals with the control of a two degrees of freedom (2-DOF) piezoelectric

actuator for precise positioning and which exhibits strong hysteresis nonlinearity and

strong cross-couplings. To tackle the nonlinearity and the cross-couplings, we propose

two decoupled models in which they are considered as (fictive) external disturbances

which require proper characterization. Then, a backstepping technique is proposed to

construct a robust controller that merges sliding-mode and adaptive schemes. Extensive

experimental tests are finally carried out to prove the efficiency of the modeling and

control technique proposed.

Journal Updates:

June 2015

Automatica

Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of

the marginal likelihood estimator

Volume 58, August 2015, Pages 106–117

Gianluigi Pillonetto, Alessandro Chiuso

Department of Information Engineering, University of Padova, Padova, Italy

Abstract

Kernel-based regularization approaches have been successfully applied in the last years for regression purposes.

Recently, these machine learning techniques have been also introduced in linear system identification, by interpreting

impulse response estimation as a function learning problem. The adopted estimator solves a regularized least squares

problem which admits also a Bayesian interpretation where the impulse response is modeled as a zero-mean Gaussian

vector. A possible choice for the covariance is the so called stable spline kernel. It includes information on smoothness

and exponential stability, containing just two unknown parameters which can be tuned via marginal likelihood (ML)

optimization. Experimental evidence has shown that this new approach may outperform traditional system

identification approaches, such as PEM and subspace techniques.

The aim of this work is to provide new insights on the stable spline estimator equipped with ML estimation of

hyperparameters. To this purpose, we study the mean squared error properties of the ML procedure for

hyperparameter estimation; in doing so we shall not assume the correctness of the Bayesian priors. Then, we derive the

notion of excess degrees of freedom. This notion measures the additional complexity to be assigned to an estimator

which is also required to determine hyperparameters from data. The conclusion of our investigation is that much of

criticisms reported in the literature to robustness of ML is not well founded. On the contrary, in many situations ML can

well balance data fit and excess degrees of freedom. Hence, it turns out an important tool for tuning model complexity

in linear system identification also when undermodeling affects the kernel-based description of the impulse response.

Design of continuous–discrete observers for time-varying nonlinear systems

Volume 57, July 2015, Pages 135–144

Frédéric Mazenc, Vincent Andrieu, Michael Malisoff,

EPI DISCO INRIA-Saclay, the Laboratoire des Signaux et Systèmes (L2S, UMR CNRS 8506), CNRS, CentraleSupélec,

Université Paris-Sud, 3 rue Joliot Curie, 91192, Gif-sur-Yvette, France

LAGEP, Université de Lyon, Domaine Universitaire de la Doua, CPE, Batiment G, 2ème étage, 43 bd du 11 Novembre

1918, 69622 Villeurbanne Cedex, France

Fachbereich C - Mathematik und Naturwissenschaften, Bergische Universität Wuppertal, Fachbereich C - Mathematik

und Naturwissenschaften, Arbeitsgruppe Funktionalanalysis, Gaußstraße 20, 42097, Wuppertal, Germany

Department of Mathematics, Louisiana State University, Baton Rouge, LA 70803-4918, USA

Abstract

We present a new design for continuous–discrete observers for a large class of continuous time nonlinear time-varying

systems with discrete time measurements. Using the notion of cooperative systems, we show that the solutions of the

observers converge to the solutions of the original system, under conditions on the nonlinear terms and on the largest

sampling interval. Our conditions are given by explicit expressions.

Robust global trajectory tracking for a class of underactuated vehicles (Brief paper)

Volume 58, August 2015, Pages 90–98

Pedro Casau, Ricardo G. Sanfelice, Rita Cunha, David Cabecinhas, Carlos Silvestre,

Department of Electrical and Computer Engineering, Laboratory for Robotics and Systems in Engineering and Science

(LARSyS), Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal

Department of Computer Engineering, University of California, Santa Cruz, CA 95064, USA

Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa,

Macau, China

Abstract

In this paper, we tackle the problem of trajectory tracking for a particular class of underactuated vehicles with full

torque actuation and a single force direction (thrust), which is fixed relative to a body attached frame. Additionally, we

consider that thrust reversal is not available. Under some given assumptions, the control law that we propose is able to

track a smooth reference position trajectory while minimizing the angular distance to a desired orientation. This

objective is achieved robustly, with respect to bounded state disturbances, and globally, in the sense that it is achieved

regardless of the initial state of the vehicle. The proposed controller is tested in an experimental setup, using a small

scale quadrotor vehicle and a motion capture system.

A backstepping approach to the output regulation of boundary controlled parabolic PDEs

Volume 57, July 2015, Pages 56–64

Joachim Deutscher

Lehrstuhl für Regelungstechnik, Universität Erlangen-Nürnberg, Cauerstraße 7, D-91058 Erlangen, Germany

Abstract

In this article the output regulation problem for boundary controlled parabolic systems with spatially varying coefficients

is solved by applying the backstepping approach. Thereby, the outputs to be controlled are not required to be

measurable and can be pointwise, distributed or boundary quantities, whereas the measurement is located at the

boundary. By solving the state feedback regulator problem in the backstepping coordinates regulator equations with a

simple structure result, so that their analysis and solution is facilitated. The output feedback regulator design is

completed by determining a finite-dimensional reference observer and an infinite-dimensional disturbance observer. For

the latter a backstepping approach is presented that consists of a triangular decoupling in the backstepping coordinates.

This allows a systematic design and the explicit derivation of directly verifiable existence conditions for the disturbance

observer. It is shown that for the resulting compensator the separation principle holds implying output regulation for the

exponentially stable closed-loop system with a prescribed stability margin. The output regulation results of the article

are illustrated by means of a parabolic system with an in-domain pointwise controlled output.

System & Control Letters

Distributed finite-time tracking for a multi-agent system under a leader with bounded unknown

acceleration

Volume 81, July 2015, Pages 8–13

Yu Zhao, Zhisheng Duan, Guanghui Wen, Guanrong Chen,

State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, College

of Engineering, Peking University, Beijing 100871, China

Department of Mathematics, Southeast University, Nanjing 210096, China

Department of Electronic Engineering, City University of Hong Kong, Hong Kong Special Administrative Region

Abstract

This paper addresses the distributed finite-time tracking problem for a group of mobile agents modeled by double-

integrator dynamics under a leader with bounded unknown acceleration. First, a distributed finite-time tracking protocol

is designed based on both the relative position and the relative velocity measurements. This protocol can drive the

states of the followers to track the leader in finite time under the constraint that the leader’s acceleration is bounded

but unknown to the followers. Then, a novel position-based tracking protocol is designed and analyzed for solving the

distributed finite-time tracking problem when both velocity and acceleration measurements are not available for the

followers. It is theoretically proved that the followers can move to be with the leader in finite time if the network

topology is undirected among the followers but has a directed path from the leader to each follower. In particular, the

position-based protocol does not require the relative input information between the agents. Finally, the effectiveness of

the algorithms is illustrated by numerical simulations.

IEEE Transactions on Robotics

Unified Terrain Mapping Model With Markov Random Fields

Volume:31 , Issue: 2 , Pages: 290 – 306

Tse, R. ; Ahmed, N.R. ; Campbell, M.

Autonomous Syst. Lab., Cornell Univ., Ithaca, NY, USA

Abstract

A terrain mapping model is proposed using a generalized Markov random field (MRF) representation. Unlike previous

work, the proposed MRF can fully represent uncertainties due to sensor pose and measurement errors, as well as data

association errors in a single model. Additionally, neither homoscedasticity nor a predefined shape of the likelihood

distribution is assumed. The flexibility of an MRF model allows spatial height correlations to be incorporated. The ability

to include spatial correlations not only improves the accuracy through the benefits of Bayesian prior modeling, but also

serves as a basis for terrain property characterization. Maximum likelihood solutions of terrain roughness are derived.

Benefits of the proposed model are demonstrated experimentally on indoor and outdoor datasets. Results show that

the MRF model leads to lower height estimation errors. In addition, the capability of estimating non-Gaussian height

distributions allows the information about individual terrain features to be preserved. Finally, the model is able to

accurately estimate the roughness of the terrain, which is beneficial for edge detection of obstacles and nontraversible

terrain regions.

Multirobot Control Using Time-Varying Density Functions

Volume:31 , Issue: 2; Pages: 489 – 493

Lee, S.G. ; Diaz-Mercado, Y. ; Egerstedt, M.

Dept. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

Abstract

An approach is presented for influencing teams of robots by means of time-varying density functions, representing

rough references for where the robots should be located. A continuous-time coverage algorithm is proposed and

distributed approximations are given whereby the robots only need to access information from adjacent robots. Robotic

experiments show that the proposed algorithms work in practice, as well as in theory.