<![CDATA[ IEEE Transactions on Automatic Control - new TOC ]]>
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TOC Alert for Publication# 9 2016August 22<![CDATA[Table of Contents]]>618C1C457<![CDATA[IEEE Transactions on Automatic Control publication information]]>618C2C238<![CDATA[Scanning the Issue]]>6182017201850<![CDATA[Distributed Consensus of Second-Order Multi-Agent Systems With Heterogeneous Unknown Inertias and Control Gains Under a Directed Graph]]> - modification schemes for the gain adaptation are proposed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing consensus convergence. Furthermore, we show that one proposed algorithm also works for consensus of agents with intrinsic Lipschitz nonlinear dynamics. The control gains are varying and updated by distributed adaptive laws. As a result, the proposed algorithms require no global information and thus can be implemented in a fully distributed manner.]]>618201920341410<![CDATA[Approximate Kalman-Bucy Filter for Continuous-Time Semi-Markov Jump Linear Systems]]>61820352048816<![CDATA[On Feedback Architectures With Zero-Vibration Signal Shapers]]>618204920641245<![CDATA[Dissipativity-Based Small-Gain Theorems for Stochastic Network Systems]]>61820652078444<![CDATA[Event-Triggered State Observers for Sparse Sensor Noise/Attacks]]>618207920911201<![CDATA[A General Framework for Robust Output Synchronization of Heterogeneous Nonlinear Networked Systems]]>61820922107771<![CDATA[A Notion of Robustness for Cyber-Physical Systems]]>input-output dynamical stability for cyber-physical systems (CPS) which merges existing notions of robustness for continuous systems and discrete systems. The notion captures two intuitive aims of robustness: bounded disturbances have bounded effects and the consequences of a sporadic disturbance disappear over time. We present a design methodology for robust CPS which is based on an abstraction and refinement process. We suggest several novel notions of simulation relations to ensure the soundness of the approach. In addition, we show how such simulation relations can be constructed compositionally. The different concepts and results are illustrated throughout the paper with examples.]]>61821082123472<![CDATA[Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems]]>618212421391111<![CDATA[A Uniform Approach for Synthesizing Property-Enforcing Supervisors for Partially-Observed Discrete-Event Systems]]>618214021541282<![CDATA[Motion Planning for Continuous-Time Stochastic Processes: A Dynamic Programming Approach]]>618215521701452<![CDATA[A Characterization of the Minimal Average Data Rate That Guarantees a Given Closed-Loop Performance Level]]>61821712186914<![CDATA[On the Steady-State Control of Timed Event Graphs With Firing Date Constraints]]>61821872202612<![CDATA[Characterization and Optimization of <inline-formula><tex-math notation="LaTeX">$l_{infty}$</tex-math></inline-formula> Gains of Linear Switched Systems]]> gain characterizations of linear switched systems (LSS) and present various relevant results on their exact computation and optimization. Depending on the role of the switching sequence, we study two broad cases: first, when the switching sequence attempts to maximize, and second, when it attempts to minimize the gain. The first, named as worst-case throughout the paper, can be related to robustness of the system to uncontrolled switching; the second relates to situations when the switching can be part to the overall decision making. Although, in general, the exact computation of gains is difficult, we provide specific classes, the input-output switching systems, for which it is shown that linear programming can be used to obtain the worst-case gain. This is a sufficiently rich class of systems as any stable LSS can be approximated by one. Certain applications to robust control design are provided where we show that a switched compensation independently of the plant has no advantage over a linear time invariant (LTI) compensation, and further, if the plant is strictly causal, even a switched compensation which has a matched switching with the plant does not provide a better performance over an LTI compensation. Also, we present a new necessary and sufficient condition to check the stability of LSS in form of a model matching problem. On the other hand, if one is interested in minimizing the gain over the switching sequences, we show that, for finite impulse response (FIR) switching systems the minimizing switching sequence-
can be chosen to be periodic. For input-only or output-only switching an exact, readily computable, characterization of the minimal gain is provided, and it is shown that the minimizing switching sequence is constant, which, as also shown, is not true for input-output switching.]]>61822032218503<![CDATA[An IQC Approach to Robust Stability of Aperiodic Sampled-Data Systems]]>61822192225267<![CDATA[A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control]]>618222622311347<![CDATA[Containment Control of Single-Integrator Network With Limited Communication Data Rate]]>61822322238360<![CDATA[On Positive-Realness and Lyapunov Functions for Switched Linear Differential Systems]]>61822392244206<![CDATA[Reference Tracking With Guaranteed Error Bound for Constrained Linear Systems]]>61822452250487<![CDATA[On Minimal Spectral Factors With Zeroes and Poles Lying on Prescribed Regions]]>61822512255159<![CDATA[Finite-Time Synchronization of Coupled Networks With Markovian Topology and Impulsive Effects]]> -matrix technique and designing new Lyapunov functions and controllers, sufficient conditions are derived to ensure the synchronization within a setting time, and the conditions do not contain any uncertain parameter. It is demonstrated theoretically and numerically that the number of consecutive impulses with minimum impulsive interval of the desynchronizing impulsive sequence should not be too large. It is interesting to discover that the setting time is related to initial values of both the network and the Markov chain. Numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.]]>61822562261287<![CDATA[Feedback Linearization for Nonlinear Systems With Time-Varying Input and Output Delays by Using High-Gain Predictors]]>61822622268387<![CDATA[Properties of Composite Laplacian Quadratics and Their Applications in Consensus of Linear Differential Inclusions]]>618226922753570<![CDATA[Maximum Likelihood Estimation of the Non-Parametric FRF for Pulse-Like Excitations]]>61822762281679<![CDATA[Slide Window Bounded-Error Time-Varying Systems Identification]]>61822822287435<![CDATA[Backstepping Design of Robust Output Feedback Regulators for Boundary Controlled Parabolic PDEs]]>61822882294243<![CDATA[Frequency-Domain Analysis of Control Loops With Intermittent Data Losses]]>61822952300829<![CDATA[On the Characterization of Local Nash Equilibria in Continuous Games]]>differential Nash equilibria. Further, we provide a sufficient condition (non-degeneracy) guaranteeing differential Nash equilibria are isolated and show that such equilibria are structurally stable. We present tutorial examples to illustrate our results and highlight degeneracies that can arise in continuous games.]]>61823012307184<![CDATA[A Comparison of LQR Optimal Performance in the Decentralized and Centralized Settings]]>61823082311129<![CDATA[Crowd-Averse Cyber-Physical Systems: The Paradigm of Robust Mean-Field Games]]> - optimal control that tries to capture the mutual influence between a crowd and its individuals. First, we establish a mean-field system for such games including the effects of adversarial disturbances. Second, we identify the optimal response of the individuals for a given population behavior. Third, we provide an analysis of equilibria and their stability.]]>61823122317339<![CDATA[Global Asymptotic Stabilization of Nonlinear Deterministic Systems Using Wiener Processes]]>61823182323485<![CDATA[Introducing IEEE Collabratec]]>61823242324542<![CDATA[IEEE Control Systems Society Information]]>618C3C350