<![CDATA[ IEEE Transactions on Automatic Control - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 9 2017September25<![CDATA[Table of Contents]]>629C1430151<![CDATA[IEEE Control Systems Society]]>629C2C2144<![CDATA[Scanning the Issue*]]>6294302430492<![CDATA[Asynchronous Networked MPC With ISM for Uncertain Nonlinear Systems]]>62943054317713<![CDATA[Optimality Conditions for Long-Run Average Rewards With Underselectivity and Nonsmooth Features]]>62943184332343<![CDATA[Why RLC Realizations of Certain Impedances Need Many More Energy Storage Elements Than Expected]]>629433343461445<![CDATA[On the Relation Between the Minimum Principle and Dynamic Programming for Classical and Hybrid Control Systems]]>62943474362399<![CDATA[Robust Output Regulation for Continuous-Time Periodic Systems]]>62943634375761<![CDATA[String Stability and a Delay-Based Spacing Policy for Vehicle Platoons Subject to Disturbances]]>629437643911712<![CDATA[Preserving Strong Connectivity in Directed Proximity Graphs]]>62943924404918<![CDATA[On the Convergence of a Distributed Augmented Lagrangian Method for Nonconvex Optimization]]>62944054420687<![CDATA[Asynchronous Multiagent Primal-Dual Optimization]]>62944214435608<![CDATA[Multivalued Robust Tracking Control of Lagrange Systems: Continuous and Discrete-Time Algorithms]]>62944364450972<![CDATA[Robust Tracking Commitment]]>62944514466733<![CDATA[Modeling and Control of Stochastic Systems With Poorly Known Dynamics]]>62944674482579<![CDATA[Data-Dependent Convergence for Consensus Stochastic Optimization]]>629448344981485<![CDATA[Fast Moving Horizon State Estimation for Discrete-Time Systems Using Single and Multi Iteration Descent Methods]]>629449945111211<![CDATA[Strong Stationarity Conditions for Optimal Control of Hybrid Systems]]>62945124526562<![CDATA[Design of Integral Controllers for Nonlinear Systems Governed by Scalar Hyperbolic Partial Differential Equations]]>62945274536406<![CDATA[Dynamic Control of Agents Playing Aggregative Games With Coupling Constraints]]>62945374548737<![CDATA[An Approximate Dynamic Programming Approach to Multiagent Persistent Monitoring in Stochastic Environments With Temporal Logic Constraints]]>62945494563861<![CDATA[Optimal Estimation and Control for Lossy Network: Stability, Convergence, and Performance]]>62945644579830<![CDATA[Likelihood Analysis of Power Spectra and Generalized Moment Problems]]>62945804592444<![CDATA[On Lyapunov-Metzler Inequalities and S-Procedure Characterizations for the Stabilization of Switched Linear Systems]]>62945934597180<![CDATA[Foundations of a Bicoprime Factorization Theory]]>62945984603262<![CDATA[Sliding Mode Control of Discrete-Time Switched Systems with Repeated Scalar Nonlinearities]]>∞ gain performance is considered for the system dynamics to optimize its transient state performance. First, sufficient conditions are given to guarantee the corresponding system is exponentially stable while achieving a desired weighed ℋ_{∞} performance. A new switching surface function is constructed by the average dwell time technique and the positive diagonally dominant Lyapunov functional method to further reduce the conservativeness induced by the repeated scalar nonlinearity. Then, the corresponding sliding mode dynamics are obtained and the solvability condition for the desired switching surface function is derived. Furthermore, the synthesis of the proposed SMC law is proposed to force the resulting closed-loop system trajectories onto the pre-specified sliding mode region with a desired level of accuracy. Finally, the feasibility and the effectiveness of the presented new design techniques are illustrated by examples and simulations.]]>62946044610428<![CDATA[Optimal Sensor Data Scheduling for Remote Estimation Over a Time-Varying Channel]]>62946114617488<![CDATA[Dynamic Attack Detection in Cyber-Physical Systems With Side Initial State Information]]>62946184624268<![CDATA[Stabilizing Quantum States and Automatic Error Correction by Dissipation Control]]>62946254630376<![CDATA[Reverse and Forward Engineering of Frequency Control in Power Networks]]>62946314638435<![CDATA[An Auxiliary Particle Filtering Algorithm With Inequality Constraints]]>62946394646509<![CDATA[Coordination Over Multi-Agent Networks With Unmeasurable States and Finite-Level Quantization]]>62946474653246<![CDATA[Adaptive Consensus of Nonlinear Multi-Agent Systems With Non-Identical Partially Unknown Control Directions and Bounded Modelling Errors]]>62946544659484<![CDATA[MAS Consensus and Delay Limits Under Delayed Output Feedback]]>62946604666399<![CDATA[Realization Theory for LPV State-Space Representations With Affine Dependence]]>62946674674292<![CDATA[Robust Transport Over Networks]]>62946754682247<![CDATA[Distributed Adaptive Consensus Output Regulation of Network-Connected Heterogeneous Unknown Linear Systems on Directed Graphs]]>62946834690291<![CDATA[Necessary Stability Conditions for Neutral Type Systems With a Single Delay]]>62946914697509<![CDATA[Predictive Networked Control of Discrete Event Systems]]>62946984705265<![CDATA[Relaxed Conditions for the Input-to-State Stability of Switched Nonlinear Time-Varying Systems]]>62947064712225<![CDATA[Estimation of Sampling Period for Stochastic Nonlinear Sampled-Data Systems With Emulated Controllers]]>62947134718258<![CDATA[A General Dynamic Scaling Based Control Redesign to Handle Input Unmodeled Dynamics in Uncertain Nonlinear Systems]]>62947194726246<![CDATA[Stabilization of Uncertain Discrete-Time Linear System With Limited Communication]]>62947274733359<![CDATA[Non-Intrusive Reference Governors for Over-Actuated Linear Systems]]>62947344740517<![CDATA[Synchronization of Multiagent Systems Using Event-Triggered and Self-Triggered Broadcasts]]>62947414746417<![CDATA[Global Chartwise Feedback Linearization of the Quadcopter With a Thrust Positivity Preserving Dynamic Extension]]>62947474752394<![CDATA[Quantized/Saturated Control for Sampled-Data Systems Under Noisy Sampling Intervals: A Confluent Vandermonde Matrix Approach]]>62947534759499<![CDATA[Boundary Stabilization of Wave Equation With Velocity Recirculation]]>62947604767441<![CDATA[Event-Triggered State Estimation With an Energy Harvesting Sensor]]>62947684775543<![CDATA[Low-Gain Integral Control for Multi-Input Multioutput Linear Systems With Input Nonlinearities]]>62947764783624<![CDATA[Local Condition Based Consensus Filtering With Stochastic Nonlinearities and Multiple Missing Measurements]]>∞-consensus filtering problem for a class of discrete time-varying systems with stochastic nonlinearities and multiple missing measurements. The stochastic nonlinearities are formulated by statistical means and the missing measurements are characterized by a set of random variables obeying Bernoulli distribution. A novel H_{∞}-consensus performance index is proposed to measure both the filtering accuracy of every node and the consensus among neighbor nodes. Then, a new concept called stochastic vector dissipativity is proposed wherein the dissipation matrix is formulated by a nonsingular substochastic matrix, which is skillfully constructed by a new defined interval function on the outdegree. A set of local sufficient conditions in terms of the recursive linear matrix inequalities is presented for each node such that the proposed H_{∞}-consensus performance can be guaranteed for the local augmented dynamics over the finite horizon. Furthermore, a novel algorithm proposed here can be implemented on each node. Finally, an illustrative simulation is presented to demonstrate the effectiveness and applicability of the proposed algorithm.]]>62947844790486<![CDATA[Stability Analysis of Impulsive Stochastic Nonlinear Systems]]>62947914797308<![CDATA[Parameter and Controller Dependent Lyapunov Functions for Robust D-Stability and Robust Performance Controller Design]]>62947984803254<![CDATA[Inverse Feedback Shapers for Coupled Multibody Systems]]>62948044810685<![CDATA[Distributed Nash Equilibrium Seeking by a Consensus Based Approach]]>62948114818503<![CDATA[Stability and Bifurcation of Delayed Fractional-Order Dual Congestion Control Algorithms]]>62948194826781<![CDATA[On Minimizing the Maximal Characteristic Frequency of a Linear Chain]]>62948274833547<![CDATA[The Analytic Solutions of the Homogeneous Modified Algebraic Riccati Equation]]>62948344839298<![CDATA[Distributed Fault Detection Isolation and Accommodation for Homogeneous Networked Discrete-Time Linear Systems]]>62948404847398<![CDATA[Joint State Estimation and Delay Identification for Nonlinear Systems With Delayed Measurements]]>62948484854568<![CDATA[Distributed Global Output-Feedback Control for a Class of Euler–Lagrange Systems]]>62948554861493<![CDATA[The Effect of Uncertainty on Production-Inventory Policies With Environmental Considerations]]>62948624868615<![CDATA[SVD-Based Kalman Filter Derivative Computation]]>T factorization-based (UD-based) methods. They imply the Cholesky decomposition of the corresponding error covariance matrix. Another important matrix factorization method is the singular value decomposition (SVD) and, hence, further encouraging KF algorithms might be found under this approach. Meanwhile, the filter sensitivity computation heavily relies on the use of matrix differential calculus. Previous works on the robust KF derivative computation have produced the SR- and UD-based methodologies. Alternatively, in this paper, we design the SVD-based approach. The solution is expressed in terms of the SVD-based KF covariance quantities and their derivatives (with respect to unknown system parameters). The results of numerical experiments illustrate that although the newly developed SVD-based method is algebraically equivalent to the conventional approach and the previously derived SR- and UD-based strategies, it outperforms the mentioned techniques for estimation accuracy in ill-conditioned situations.]]>62948694875234<![CDATA[Verifiable Conditions for Multioutput Observer Error Linearizability]]>62948764883268<![CDATA[On ${\mathcal H}_{\infty }$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks]]>∞ filtering problem for a class of time-varying nonlinear delayed system under high-rate communication network and stochastic protocol (SP). The communication between the sensors and the state estimator is implemented via a shared high-rate communication network in which multiple transmissions are generated between two adjacent sampling instants of sensors. At each transmission instant, only one sensor is allowed to get access to the communication network in order to avoid data collisions and the SP is employed to determine which sensor obtains access to the network at a certain instant. The mapping technology is applied to characterize the randomly switching behavior of the data transmission resulting from the utilization of the SP. The aim of the problem addressed is to design an estimator such that the H_{∞} disturbance attenuation level is guaranteed for the estimation error dynamics over a given finite horizon. Sufficient conditions are derived for the existence of the finite-horizon filter satisfying the prescribed H_{∞} performance requirement, and the explicit expression of the time-varying filter gains is characterized by resorting toa set of recursive matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filter design scheme.]]>62948844890479<![CDATA[Effect of Adding Edges to Consensus Networks With Directed Acyclic Graphs]]>62948914897428<![CDATA[Introducing IEEE Collabratec]]>629489848981930<![CDATA[Become a published author in 4 to 6 weeks]]>629489948991043<![CDATA[IEEE Global History Network]]>629490049003145<![CDATA[IEEE Control Systems Society]]>629C3C3178