<![CDATA[ IEEE Transactions on Automatic Control - new TOC ]]>
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TOC Alert for Publication# 9 2018April 26<![CDATA[Table of Contents]]>635C1C443<![CDATA[IEEE Control Systems Society]]>635C2C2159<![CDATA[Scanning the Issue]]>6351229123088<![CDATA[Nonconservative Discrete-Time ISS Small-Gain Conditions for Closed Sets]]>closed sets. Toward this end, we first develop a Lyapunov characterization of $omega$ISS via finite-step $omega$ISS Lyapunov functions. Then, we provide the small-gain conditions to guarantee $omega$ISS of a network of systems. Finally, applications of our results to partial ISS, ISS of time-varying systems, synchronization problems, incremental stability, and distributed observers are given.]]>63512311242515<![CDATA[Convex Lifting: Theory and Control Applications]]>convex lifting, which will be proven to enable significant implementation benefits for the class of piecewise affine controllers. Accordingly, two different algorithms to construct a convex lifting for a given polyhedral/polytopic partition will be presented. These two algorithms rely on either the vertex or the halfspace representation of the related polyhedra. Also, we introduce an algorithm to refine a polyhedral partition, which does not admit a convex lifting, into a convexly liftable one. Furthermore, two different schemes will be put forward to considerably reduce both the memory footprint and the online evaluation effort, which play a key role in implementation of piecewise affine controllers. Finally, these results will be illustrated via numerical examples and a complexity analysis.]]>635124312581055<![CDATA[Computation of Lyapunov Functions for Nonlinear Differential Equations via a Massera-Type Construction]]>$mathcal {K}_{infty}$ -function of the norm of the solution of the system. In addition, we show how the developed converse theorem can be used to construct an estimate of the domain of attraction. Finally, a range of examples from the literature and biological applications, such as the genetic toggle switch, the repressilator, and the HPA axis, are worked out to demonstrate the efficiency and improvement in computation of the proposed approach.]]>63512591272787<![CDATA[Resource Allocation Game Under Double-Sided Auction Mechanism: Efficiency and Convergence]]>$mathcal {O}(ln (1/varepsilon))$ with $varepsilon$ representing the termination criterion of the algorithm.]]>635127312871009<![CDATA[A Novel Reduced Model for Electrical Networks With Constant Power Loads]]>63512881299603<![CDATA[Unicycle With Only Range Input: An Array of Patterns]]>635130013122324<![CDATA[The Role of Symmetry in Rigidity Analysis: A Tool for Network Localization and Formation Control]]>$mathbb {R}^2$ or $mathbb {R}^3$ is rigidly constrained by interagent distances up to rigid-body transformations of space) is inherently dependent on the nature of Euclidean space and the nature of distance measurements. In this paper, we present a generalized formulation of rigidity, where agent states may lie in heterogeneous and non-Euclidean state spaces with arbitrary differentiable measurement constraints. A key aspect of our approach is to recognize the crucial role that the symmetry action of rigid-body transformations plays in classical rigidity theory. We consider a general symmetry given by a Lie-group action on a heterogeneous state space and define global rigidity of a formation to be the case where the interagent measurements fully constrain the agent locations up to the invariance encoded by the group action. In this framework, we develop general definitions of local rigidity and infinitesimal rigidity and introduce a new notion of robust rigidity that we believe will be important for control applications. To motivate the development, we show how the proposed theory can be applied to generalizations of the established problems of network localization and formation control. The provided results are directly applicable to networks of robotic vehicles involving a mixture of bearing and distance sensors, as well as cases where a collection of ground, submersible, and aerial vehicles operate in a single formation.]]>63513131328623<![CDATA[ADD-OPT: Accelerated Distributed Directed Optimization]]>directed graph. The proposed algorithm, Accelerated Distributed Directed OPTimization (ADD-OPT), achieves the best known convergence rate for this class of problems, $O(mu ^{k}),0<mu <1$, given strongly convex, objective functions with globally Lipschitz-continuous gradients, where $k$ is the number of iterations. Moreover, ADD-OPT supports a wider and more realistic range of step sizes in contrast to existing work. In particular, we show that ADD-OPT converges for arbitrarily small (positive) step sizes. Simulations further illustrate our results.]]>63513291339814<![CDATA[Solving the Dual Problems of Dynamic Programs via Regression]]>63513401355440<![CDATA[Convergence of Limited Communication Gradient Methods]]>635135613711400<![CDATA[Distributed Constrained Optimization and Consensus in Uncertain Networks via Proximal Minimization]]>635137213871407<![CDATA[A Quantum Hamiltonian Identification Algorithm: Computational Complexity and Error Analysis]]>$O(d^6)$, where $d$ is the dimension of the system Hamiltonian. An error upper bound $O(frac{d^3}{sqrt{N}})$ is also established, where $N$ is the resource number for the tomography of each output state, and several numerical examples demonstrate the effectiveness of the proposed TSO Hamiltonian identification method.]]>63513881403686<![CDATA[A Hybrid Design Approach for Output Feedback Exponential Stabilization of Markovian Jump Systems]]>63514041417876<![CDATA[Cyber-Physical Attacks With Control Objectives]]>63514181425454<![CDATA[A Fractional-Order Repetitive Controller for Periodic Disturbance Rejection]]>635142614331086<![CDATA[Distributed Adaptive Convex Optimization on Directed Graphs via Continuous-Time Algorithms]]>635143414411590<![CDATA[Safe Markov Chains for ON/OFF Density Control With Observed Transitions]]>63514421449820<![CDATA[Robust Exponential Stability and Disturbance Attenuation for Discrete-Time Switched Systems Under Arbitrary Switching]]>$M$-step sequence with sufficient length and a family of Lyapunov functions, and then a stability criterion is proposed for the nominal linear case in the framework of quadratic Lyapunov function. In order to extend the stability criterion to handle uncertainties, an equivalent condition which has a promising feature that is convex in system matrices is derived, leading to a robust stability criterion for uncertain discrete-time switched linear systems. Moreover, also taking the advantage of the convex feature, the disturbance attenuation performance in the sense of $ell _2$-gain is studied. Several numerical examples are provided to illustrate our approach.]]>63514501456383<![CDATA[Synthesis of Similarity Enforcing Supervisors for Nondeterministic Discrete Event Systems]]>635145714641152<![CDATA[Analysis of Gradient Descent Methods With Nondiminishing Bounded Errors]]> $text{GD}$) algorithms with gradient errors that do not necessarily vanish, asymptotically. In particular, sufficient conditions are presented for both stability (almost sure boundedness of the iterates) and convergence of $text{GD}$ with bounded (possibly) nondiminishing gradient errors. In addition to ensuring stability, such an algorithm is shown to converge to a small neighborhood of the minimum set, which depends on the gradient errors. It is worth noting that the main result of this paper can be used to show that $text{GD}$ with asymptotically vanishing errors indeed converges to the minimum set. The results presented herein are not only more general when compared to previous results, but our analysis of $text{GD}$ with errors is new to the literature to the best of our knowledge. Our work extends the contributions of Mangasarian and Solodov, Bertsekas and Tsitsiklis, and Tadić and Doucet. Using our framework, a simple yet effective implementation of $text{GD}$ using simultaneous perturbation stochastic approximations, with constant sensitivity parameters, is presented. Another important improvement over many previous results is that there are no “additional” restrictions imposed on the step sizes. In machine learning applications where step sizes are related to learning rates, our assumptions, unlike those of other papers, do not affect these learning rates. Finally, we present experimental results to validate our theory.]]>63514651471422<![CDATA[Two-Dimensional Peak-to-Peak Filtering for Stochastic Fornasini–Marchesini Systems]]>63514721479665<![CDATA[Robust Regulation of Infinite-Dimensional Port-Hamiltonian Systems]]>63514801486356<![CDATA[Stability Analysis for Positive Singular Systems With Time-Varying Delays]]>63514871494432<![CDATA[Asymptotic Regulation of Time-Delay Nonlinear Systems With Unknown Control Directions]]>63514951502397<![CDATA[On the Design of Output Feedback Controllers for LTI Systems Over Fading Channels]]>63515031508315<![CDATA[A General Approach to Coordination Control of Mobile Agents With Motion Constraints]]>63515091516644<![CDATA[The Quadratic Regulator Problem and the Riccati Equation for a Process Governed by a Linear Volterra Integrodifferential Equations]]>$mathbf{mathbb{R}}^n$ . Our main goal is the proof that it is possible to associate a Riccati differential equation to this quadratic control problem, from which the feedback form of the optimal control can be computed. This is in contrast with previous papers on the subject, which confine themselves to study the Fredholm integral equation which is solved by the optimal control.]]>63515171522235<![CDATA[Constructive Nonlinear Internal Models for Global Robust Output Regulation and Application]]>global robust output regulation with uncertain exosystems. That is, the globally stabilizing control for the augmented system can be done independent of adaptive stabilization control law. We also solve a cooperative global robust output regulation problem as an interesting application of the proposed internal models. In this way, we note that the hurdles can be circumvented arising in the same problem if the canonical linear internal model were used.]]>63515231530638<![CDATA[Very Strictly Passive Controller Synthesis With Affine Parameter Dependence]]>${boldsymbol{mathcal {H}_{infty} }}$ -inspired control synthesis method is presented along with an experimental comparison between the proposed VSP-LPV controller and a self-scheduled LPV controller. Experimental results demonstrate effective joint angle and rate tracking of a two-link flexible-joint manipulator when the proposed VSP-LPV controller is used.]]>63515311537785<![CDATA[Stochastic Super-Twist Sliding Mode Controller]]> $V_{t}=Vleft(x_{t},y_{t}right)$ (Polyakov–Poznyak, Moreno–Osorio, Orlov and Utkin) designed for the stability analysis of the deterministic version of super-twist controllers. The major finding is that under stochastic (in fact, unbounded) perturbations, the special selection of a gain-parameter of such controller, making it depending on $V_{t}$ and its gradient $frac{ partial V}{partial y}left(x_{t},y_{t}right)$ , provides the controller with an adaptivity property and guarantees the mean-square convergence of $V_{t}$ into the prespecified zone around the origin which depends on the diffusion parameter of stochastic noise, the upper estimate of the second derivative $frac{partial ^{2}V}{partial y^{2}}$ as well as on the parameters of the controller.]]>63515381544549<![CDATA[Corrections to “Multi-Sensor Kalman Filtering With Intermittent Measurements”]]>6351545154559<![CDATA[Introducing IEEE Collabratec]]>635154615461858<![CDATA[IEEE Control Systems Society]]>635C3C3166