<![CDATA[ IEEE Transactions on Automatic Control - new TOC ]]>
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TOC Alert for Publication# 9 2017December 14<![CDATA[Table of Contents]]>6212C1607353<![CDATA[IEEE Control Systems Society]]>6212C2C2144<![CDATA[Editorial The Transactions on Automatic Control and Its Tradition of Excellence]]>621260746077125<![CDATA[Scanning the Issue*]]>62126078608097<![CDATA[An Input-to-State-Stability Approach to Economic Optimization in Model Predictive Control]]>621260816093943<![CDATA[Exact Complexity Certification of Active-Set Methods for Quadratic Programming]]>621260946109795<![CDATA[An Augmented Lagrangian Filter Method for Real-Time Embedded Optimization]]>621261106121448<![CDATA[Optimizing the Convergence Rate of the Continuous-Time Quantum Consensus]]>$N$ qudits. It is shown that the optimal convergence rate is independent of the value of $d$ in qudits. By classifying the induced graphs as the Schreier graphs, they are categorized in terms of the partitions of integer $N$. The intertwining relation is established between one-level dominant partitions in the Hasse Diagram of integer $N$. Based on this result, the proof of the Aldous’ conjecture is extended to all possible induced graphs, and the original optimization problem is reduced to optimizing algebraic connectivity of the smallest induced graph. Utilizing the generalization of Aldous’ conjecture, it is shown that the convergence rates of the algorithm to both the consensus state and the reduced quantum state consensus are the same. By providing the analytical solution to semidefinite programming formulation of the obtained problem, closed-form expressions for the optimal results are provided for a range of topologies.]]>621261226135655<![CDATA[Stochastic Feedback Control With One-Dimensional Degenerate Diffusions and Nonsmooth Value Functions]]>$(sqrt{dt})^{1+gamma }$, $0<gamma <1$, with $gamma =0$ for nondegenerate points. We apply the direct-comparison-based approach to derive all the results. In all the analysis, viscosity solution is not needed.]]>621261366151422<![CDATA[Critical Connectivity and Fastest Convergence Rates of Distributed Consensus With Switching Topologies and Additive Noises]]>$delta$ (termed the extensible exponent) is proposed. With this and a balanced topology condition, we show that a critical value of $delta$ for consensus is $1/2$. Optimization on convergence rate of this protocol is further investigated. It is proved that the fastest convergence rate, which is the theoretic optimal rate among all controls, is of the order $1/t$ for the best topologies, and is of the order $1/t^{1-2delta }$ for the worst topologies, which are balanced and satisfy the extensible joint-connectivity condition. For practical implementation, certain open-loop control strategies are introduced to achieve consensus with a convergence rate of the same order as the fastest convergence rate. Furthermore, a consensus condition is derived for nonstationary and strongly correlated random topologies. The algorithms and consensus conditions are applied to distributed consensus computation of mobile ad-hoc networks; and their related critical exponents are derived from relative velocities of mobile agents for guaranteeing consensus.]]>621261526167762<![CDATA[Exponential Convergence of the Discrete- and Continuous-Time Altafini Models]]>[3], exponential convergence of the system is studied using a graphical approach. Necessary and sufficient conditions for exponential convergence with respect to each possible type of limit states are provided. Specifically, under the assumption of repeatedly jointly strong connectivity, it is shown that 1) a certain type of two-clustering will be reached exponentially fast for almost all initial conditions if, and only if, the sequence of signed digraphs is repeatedly jointly structurally balanced corresponding to that type of two-clustering; 2) the system will converge to zero exponentially fast for all initial conditions if, and only if, the sequence of signed digraphs is repeatedly jointly structurally unbalanced. An upper bound on the convergence rate is provided. The results are also extended to the continuous-time Altafini model.]]>621261686182414<![CDATA[Geometric Properties of Isostables and Basins of Attraction of Monotone Systems]]>621261836194767<![CDATA[Robust Global Adaptive Exponential Stabilization of Discrete-Time Systems With Application to Freeway Traffic Control]]>621261956208792<![CDATA[Optimal Placement of Virtual Inertia in Power Grids]]>$mathscr {H}_2$ performance metric accounting for the network coherency. The optimal inertia placement problem turns out to be non-convex, yet we provide a set of closed-form global optimality results for particular problem instances as well as a computational approach resulting in locally optimal solutions. Further, we also consider the robust inertia allocation problem, wherein the optimization is carried out accounting for the worst-case disturbance location. We illustrate our results with a three-region power grid case study and compare our locally optimal solution with different placement heuristics in terms of different performance metrics.]]>6212620962201094<![CDATA[Optimization With Affine Homogeneous Quadratic Integral Inequality Constraints]]>621262216236657<![CDATA[Adaptive Stabilization of $ 2 times 2$ Linear Hyperbolic Systems With an Unknown Boundary Parameter From Collocated Sensing and Control]]>$ 2 times 2$ linear hyperbolic partial differential equations, where sensing and actuation are restricted to the boundary anticollocated with an uncertain parameter. This is done by combining a recently derived adaptive observer for the system states and the uncertain parameter, with an adaptive control law. Proof of $ L_2$-boundedness for all signals in the closed loop is given, and the system states are proved to converge to zero pointwise in space. The theory is demonstrated in a simulation.]]>621262376249600<![CDATA[Diagonal Decoupling of Linear Systems by Static-State Feedback]]>621262506265381<![CDATA[A Distance-Based Approach to Strong Target Control of Dynamical Networks]]>6212626662771449<![CDATA[Control of Transport PDE/Nonlinear ODE Cascades With State-Dependent Propagation Speed]]>621262786293932<![CDATA[Optimal Load-Side Control for Frequency Regulation in Smart Grids]]>6212629463091556<![CDATA[Dynamic Demand and Mean-Field Games]]>smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies.]]>621263106323677<![CDATA[Synchronization on Lie Groups: Coordination of Blind Agents]]>$G$ . We employ the method of extremum seeking control for nonlinear dynamical systems defined on connected Riemannian manifolds to achieve synchronization among the agents. In this approach, each agent updates its position on $G$ by only receiving the synchronization cost function. The results are obtained by employing the notion of geodesic dithers for extremum seeking on Riemannian manifolds and their equivalent version on Lie groups and applying Taylor expansion of smooth functions on Riemannian manifolds. Due to geometrical properties of the synchronization set, we employ the method of quotient manifolds to prove the convergence of the proposed algorithm. The obtained results are applied to synchronization problems on $SE(3)$ to demonstrate the efficacy of the proposed algorithm.]]>6212632463382127<![CDATA[Cooperative Global Robust Output Regulation for Nonlinear Output Feedback Multiagent Systems Under Directed Switching Networks]]>621263396352976<![CDATA[Dimension Reduction and Feedback Stabilization for Max-Plus Linear Systems and Applications in VLSI Array Processors]]>621263536368553<![CDATA[Robust Switching Control: Stability Analysis and Application to Active Disturbance Attenuation]]>621263696376849<![CDATA[State Estimation for Stochastic Complex Networks With Switching Topology]]>621263776384442<![CDATA[Distributed Estimation From Relative and Absolute Measurements]]>621263856391452<![CDATA[On the AIMD Algorithm Under Saturation Constraints]]>621263926398521<![CDATA[Output Feedback Practical Coordinated Tracking of Uncertain Heterogeneous Multi-Agent Systems Under Switching Network Topology]]>6212639964061403<![CDATA[Stochastic Dual Averaging for Decentralized Online Optimization on Time-Varying Communication Graphs]]>$mathsf{SODA}hbox{-}mathsf{C}$ and $mathsf{SODA}hbox{-}mathsf{PS}$) of Nesterov's dual averaging method $(mathsf{DA})$ , where each agent only uses a coordinate of the noise-corrupted gradient in the dual-averaging step. We show that the expected regret bounds for both algorithms have sublinear growth of $O(sqrt{T})$, with the time horizon $T$, in scenarios when the underlying communication topology is time-varying. The sublinear regret can be obtained when the stepsize is of the form $1/sqrt{t}$ and the objective functions are Lipschitz-continuous convex functions with Lipschitz gradients, and the variance of the noisy gradients is bounded. We also provide simulation results of the proposed algorithms on sensor networks to complement our theoretical analysis.]]>621264076414416<![CDATA[Thompson Sampling for Stochastic Control: The Finite Parameter Case]]>$O(T^{-1})$, which is asymptotically optimal.]]>621264156422332<![CDATA[On Stability and Trajectory Boundedness of Lotka–Volterra Systems With Polytopic Uncertainty]]>621264236429481<![CDATA[Performance Limitations of Nonminimum Phase Affine Nonlinear Systems]]>621264306437849<![CDATA[Admissibility and Exact Observability of Observation Operators for Micro-Beam Model: Time- and Frequency-Domain Approaches]]>621264386444266<![CDATA[Modeling and Controlling an Active Constrained Layer (ACL) Beam Actuated by Two Voltage Sources With/Without Magnetic Effects]]>$B^*-$ type feedback controller, which is the total current accumulated at the electrodes for the piezoelectric layers. However, as the magnetic effects are ignored (electrostatic assumption), the closed-loop system with all mechanical feedback controllers is shown to be uniformly exponentially stable.]]>621264456450275<![CDATA[On Approximating Contractive Systems]]>621264516457256<![CDATA[GO-POLARS: A Steerable Stochastic Search on the Strength of Hyperspherical Coordinates]]>621264586465401<![CDATA[Local Lyapunov Functions for Consensus in Switching Nonlinear Systems]]>$mathbb {R}^m$ and have switching interconnection topologies. Both the first theorem, formulated in terms of the states of individual agents, and the second theorem, formulated in terms of the pairwise states for pairs of agents, can be interpreted as variants of Lyapunov's second method. The two theorems complement each other; the second provides stronger convergence results under weaker graph topology assumptions, whereas the first often can be applied in a wider context in terms of the structure of the right-hand sides of the systems. The second theorem also sheds some new light on well-known results for consensus of nonlinear systems where the right-hand sides of the agents’ dynamics are convex combinations of directions to neighboring agents. For such systems, instead of proving consensus by using the theory of contracting convex sets, a local quadratic Lyapunov function can be used.]]>621264666472386<![CDATA[Hybrid Stabilization of Linear Systems With Reverse Polytopic Input Constraints]]>621264736480657<![CDATA[Cooperative Output Regulation of Linear Multi-Agent Systems by a Novel Distributed Dynamic Compensator]]>621264816488397<![CDATA[Normalized Optimal Smoothers for a Class of Hidden Generalized Reciprocal Processes]]>Transactions concerning Bayesian smoothers developed for the class of hidden reciprocal chains (RC). Within this Bayesian setting, two important issues remained unsolved, and are the subject of this note. The first, and most significant issue concerns the extent to which the models considered in our earlier work are general in terms of the statistical nature of the processes involved. The second issue concerns the practical implementation of smoothers. In this note we offer answers to both these issues. A new class of processes called generalized reciprocal chains (GRC), which include RC as a proper subclass is defined. The note argues that GRC form a more appropriate class of models from an application point of view, in particular, inference problems in target tracking. The note also describes a method for ensuring the numerical stability of the smoother algorithm. Finally, a simple numerical example is presented, which indicates potential benefits for the use of these new models in target tracking problems when compared to Markov chain or RC models.]]>621264896496473<![CDATA[Nonlinear Eigenvalue Approach to Differential Riccati Equations for Contraction Analysis]]>621264976504284<![CDATA[Mean Square Stabilization of Linear Discrete-Time Systems Over Power-Constrained Fading Channels]]>621265056512444<![CDATA[Regularized Extended Estimation With Stabilized Exponential Forgetting]]>621265136520478<![CDATA[Finite-Time Stabilizability, Detectability, and Dynamic Output Feedback Finite-Time Stabilization of Linear Systems]]>621265216528558<![CDATA[Prescribed Performance Fault-Tolerant Control of Uncertain Nonlinear Systems With Unknown Control Directions]]>621265296535793<![CDATA[Inverse Parametric Optimization in a Set-Membership Error-in-Variables Framework]]>a posteriori validation with guaranteed methods based on interval analysis. The approach is evaluated on two well-tuned numerical examples: A discrete unicycle robot model and a planar elastica model, respectively.]]>621265366543536<![CDATA[Dynamic Event-Based Control of Nonlinear Stochastic Systems]]>621265446551515<![CDATA[Disturbance Decoupling in Hybrid Linear Systems With State Jumps]]>621265526559300<![CDATA[Limit-Cycle-Based Decoupled Design of Circle Formation Control with Collision Avoidance for Anonymous Agents in a Plane]]>target circling that all agents converge onto the circle around the target, and the second is spacing adjustment that each agent maintains the desired distance from its neighbors. Then, we propose to use a controller comprised of converging part and layout part to deal with these two subobjectives, respectively. The former part is based on a limit-cycle oscillator using only the relative position from the target, and the latter is designed by also perceiving the relative position from the agent's neighbors. An important feature of the controller is that it guarantees that no collision between agents ever takes place throughout the system's evolution. Another feature is that some of the parameters in the proposed controller have explicit physical meanings related to the agents’ rotating motion around the target, so that they can be set more reasonable and easily in real applications. Numerical simulations are given to show the effectiveness and performance of the proposed circle formation controller.]]>621265606567559<![CDATA[Structured Singular Value Analysis for Spintronics Network Information Transfer Control]]>$mu$ -design tools to reveal a crossover region in the space of controllers where objectives usually thought to be conflicting are actually concordant.]]>621265686574698<![CDATA[Riemannian Optimal Control and Model Matching of Linear Port-Hamiltonian Systems]]>$H^2$ optimal control and model matching problems of linear port-Hamiltonian systems. The controller design problems are formulated as optimization problems on the product manifold of the set of skew symmetric matrices, the manifold of the symmetric positive definite matrices, and Euclidean space. A Riemannian metric is chosen for the manifold in such a manner that the manifold is geodesically complete, i.e., the domain of the exponential map is the whole tangent space for every point on the manifold. In order to solve these problems, the Riemannian gradients of the objective functions are derived, and these gradients are used to develop a Riemannian steepest descent method on the product manifold. The geodesic completeness of the manifold guarantees that all points generated by the steepest descent method are on the manifold. Numerical experiments illustrate that our method is able to solve the two specified problems.]]>621265756581559<![CDATA[Ultimate Boundedness Control for Networked Systems With Try-Once-Discard Protocol and Uniform Quantization Effects]]>621265826588486<![CDATA[Global Adaptive Controller for Linear Systems With Unknown Input Delay]]>621265896594469<![CDATA[Pinning Control for the Disturbance Decoupling Problem of Boolean Networks]]>621265956601271<![CDATA[Solution to Discrete-Time Linear FBSDEs with Application to Stochastic Control Problem]]>621266026607234<![CDATA[Stabilization of Positive Switched Linear Systems and Its Application in Consensus of Multiagent Systems]]>621266086613301<![CDATA[Whittle Index for Partially Observed Binary Markov Decision Processes]]>$M$ out of $N$ binary Markov chains when only noisy observations of state are available, with ergodic (equivalently, long run average) reward. By passing on to the equivalent problem of controlling the conditional distribution of state given observations and controls, it is cast as a restless bandit problem and its Whittle indexability is established.]]>621266146618211<![CDATA[Stability and Performance of the SVD System]]>$mn$ subsystems to $m + n$. The SVD System provides scalable performance for systems coupled using the row-column structure. The results of this paper are the first analytical results for the SVD System when applied to linear subsystems of arbitrary order. The conclusion of all of this is that using the SVD System allows you to reduce the number of inputs while maintaining as close to the same performance as possible.]]>621266196624319<![CDATA[Stability Analysis of Output Feedback Control Systems With a Memory-Based Event-Triggering Mechanism]]>621266256632358<![CDATA[On Stochastic Sensor Network Scheduling for Multiple Processes]]>621266336640373<![CDATA[On Kalman Filtering with Compromised Sensors: Attack Stealthiness and Performance Bounds]]>621266416648527<![CDATA[Robust Hybrid Output Regulation for Linear Systems With Periodic Jumps: Semiclassical Internal Model Design]]>flow internal model, in charge of providing the correct input to achieve regulation during flows, and a jump internal model, in charge of suitably resetting the state of the regulator at each period. The proposed procedure is illustrated by its application to a physically motivated example, for which the output regulation problem is not solvable by methods appeared thus far in the literature.]]>621266496656760<![CDATA[On Problems Involving Eigenvalues for Uncertain Matrices by Structured Singular Values]]>$mu$ and the skewed structured singular value $nu$. In particular, positive definiteness conditions, maximum and minimum eigenvalues, and generalized eigenvalues of uncertain matrices are expressed using $mu$ and $nu$. The obtained results allow us to acquire information of an uncertain matrix efficiently by the existing computational tools that provide practically useful approximations to the values of $mu$ and $nu$.]]>621266576663290<![CDATA[Introducing IEEE Collabratec]]>6212666466641855<![CDATA[IEEE Control Systems Society]]>6212C3C3178