<![CDATA[ IEEE Transactions on Cybernetics - new TOC ]]>
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TOC Alert for Publication# 6221036 2017July 17<![CDATA[Table of contents]]>478C11806190<![CDATA[IEEE Transactions on Cybernetics]]>478C2C292<![CDATA[Consensus of Multiagent Systems Subject to Partially Accessible and Overlapping Markovian Network Topologies]]>$H_{\infty }$ performance level. Two examples are provided to show the effectiveness of the proposed consensus protocol.]]>478180718195272<![CDATA[Consensus of Heterogeneous Linear Multiagent Systems With Communication Time-Delays]]>47818201829890<![CDATA[Finite-Horizon ${\mathcal H}_{\infty }$ Consensus Control of Time-Varying Multiagent Systems With Stochastic Communication Protocol]]>${\mathcal {H}}_{\infty }$ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ${\mathcal {H}}_{\infty }$ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the $ {\mathcal {H}}_{ \infty }$ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.]]>47818301840723<![CDATA[Network-Based Practical Consensus of Heterogeneous Nonlinear Multiagent Systems]]>478184118511284<![CDATA[Predictive Control of Networked Multiagent Systems via Cloud Computing]]>47818521859606<![CDATA[Distributed Position-Based Consensus of Second-Order Multiagent Systems With Continuous/Intermittent Communication]]>478186018711088<![CDATA[Second-Order Consensus in Multiagent Systems via Distributed Sliding Mode Control]]>478187218811537<![CDATA[Leader-Following Consensus for High-Order Nonlinear Stochastic Multiagent Systems]]>47818821891849<![CDATA[Necessary and Sufficient Conditions for Consensus of Fractional-Order Multiagent Systems via Sampled-Data Control]]>${0<\alpha <1}$ . Two cases are considered. One is FOMASs without leader, and the other is FOMASs with a leader. For each case, by applying matrix theory and algebraic graph theory, some algebraic-type necessary and sufficient conditions based on the sampling period, the fractional-order, the coupling gain, and the structure of the network are established for achieving consensus of the system. Moreover, for the network with a dynamic leader, the sampling period, the coupling gain, and the spectrum of the Laplacian matrix are carefully devised, respectively. Finally, several simulation examples are employed to validate the effectiveness of the theoretical results.]]>478189219011424<![CDATA[Rigidity-Based Multiagent Layered Formation Control]]>478190219131359<![CDATA[Output Consensus of Heterogeneous Linear Multi-Agent Systems by Distributed Event-Triggered/Self-Triggered Strategy]]>478191419241292<![CDATA[Heuristics-Based Trust Estimation in Multiagent Systems Using Temporal Difference Learning]]>478192519351259<![CDATA[Observer-Based Event-Triggering Consensus Control for Multiagent Systems With Lossy Sensors and Cyber-Attacks]]>47819361947785<![CDATA[On the Bipartite Consensus for Generic Linear Multiagent Systems With Input Saturation]]>47819481958887<![CDATA[Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances]]>478195919692031<![CDATA[Leader-Following Consensus for Linear and Lipschitz Nonlinear Multiagent Systems With Quantized Communication]]>478197019822116<![CDATA[Ergodicity-Based Cooperative Multiagent Area Coverage via a Potential Field]]>478198319932748<![CDATA[Minimal-Approximation-Based Distributed Consensus Tracking of a Class of Uncertain Nonlinear Multiagent Systems With Unknown Control Directions]]>478199420071702<![CDATA[Non-Fragile Exponential $H_\infty $ Control for a Class of Nonlinear Networked Control Systems With Short Time-Varying Delay via Output Feedback Controller]]>$ {H_\infty }$ control problems for a class of uncertain nonlinear networked control systems (NCSs) with randomly occurring information, such as the controller gain fluctuation and the uncertain nonlinearity, and short time-varying delay via output feedback controller. Using the nominal point technique, the NCS is converted into a novel time-varying discrete time model with norm-bounded uncertain parameters for reducing the conservativeness. Based on linear matrix inequality framework and output feedback control strategy, design methods for general and optimal non-fragile exponential $ {H_\infty }$ controllers are presented. Meanwhile, these control laws can still be applied to linear NCSs and general fragile control NCSs while introducing random variables. Finally, three examples verify the correctness of the presented scheme.]]>47820082019850<![CDATA[Fuzzy Tracking Control for Nonlinear Networked Systems]]>478202020311105<![CDATA[Output Feedback Distributed Containment Control for High-Order Nonlinear Multiagent Systems]]>478203220431031<![CDATA[Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication]]>47820442057594<![CDATA[Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances]]>47820582066640<![CDATA[Betweenness Centrality-Based Consensus Protocol for Second-Order Multiagent Systems With Sampled-Data]]>478206720781607<![CDATA[Consensus Problem Over High-Order Multiagent Systems With Uncertain Nonlinearities Under Deterministic and Stochastic Topologies]]>47820792088455<![CDATA[Necessary and Sufficient Conditions for Consensus of Second-Order Multiagent Systems Under Directed Topologies Without Global Gain Dependency]]>47820892098617<![CDATA[Output Containment Control of Linear Heterogeneous Multi-Agent Systems Using Internal Model Principle]]>${H_\infty }$ criterion. Unified design procedures to solve the proposed two control protocols are presented by formulation and solution of certain local state-feedback and static output-feedback problems, respectively. Numerical simulations are given to validate the proposed control protocols.]]>47820992109909<![CDATA[Consensus Control of Nonlinear Multiagent Systems With Time-Varying State Constraints]]>478211021201812<![CDATA[Distributed Bounds on the Algebraic Connectivity of Graphs With Application to Agent Networks]]>$ {M=L+D}$ are obtained using the properties of ${M}$ -matrix and non-negative matrix under a mild assumption, where $L$ is the Laplacian matrix of the graph and $ {D={\mathrm{ diag}}\{d_{1},d_{2},\ldots ,d_{N}\}}$ with $ {d_{i}>0}$ if node $ {i}$ can access the information of the leader node and 0 otherwise. Subsequently, by virtue of the results on directed graphs, the bounds on the algebraic connectivity and spectral radius of an undirected connected graph are provided. Besides establishing these bounds, another important feature is that all these bounds are distributed in the sense of only knowing the information of edge weights’ bounds and the number of nodes in a graph, without using any information of inherent structures of the graph. Therefore, these bounds can be in some sense applied to agent networks for reducing the conservatism where control gains in control protocols depend on the eigenvalues of matrices $ {M}$ or $ {L}$ , which are global information. Also some examples are provided for corroborating the feasibility of the theoretical results.]]>47821212131887<![CDATA[Event-Based Consensus for Linear Multiagent Systems Without Continuous Communication]]>478213221422037<![CDATA[Consensus for Linear Multiagent Systems With Time-Varying Delays: A Frequency Domain Perspective]]>47821432150997<![CDATA[Neural Network-Based Adaptive Leader-Following Consensus Control for a Class of Nonlinear Multiagent State-Delay Systems]]>478215121601066<![CDATA[Collectively Rotating Formation and Containment Deployment of Multiagent Systems: A Polar Coordinate-Based Finite Time Approach]]>478216121721032<![CDATA[Consensus Tracking of Heterogeneous Discrete-Time Networked Multiagent Systems Based on the Networked Predictive Control Scheme]]>47821732184802<![CDATA[A Minimal Control Multiagent for Collision Avoidance and Velocity Alignment]]>47821852192597<![CDATA[Stability of a Class of Multiagent Tracking Systems With Unstable Subsystems]]>47821932202847<![CDATA[Pinning Control of Lag-Consensus for Second-Order Nonlinear Multiagent Systems]]>478220322112922<![CDATA[Observer-Based Consensus Tracking of Nonlinear Agents in Hybrid Varying Directed Topology]]>478221222221936<![CDATA[Output Feedback Control and Stabilization for Networked Control Systems With Packet Losses]]>47822232234488<![CDATA[Event-Driven Control for Networked Control Systems With Quantization and Markov Packet Losses]]>47822352243753<![CDATA[Networked Predictive Control for Nonlinear Systems With Arbitrary Region Quantizers]]>478224422551246<![CDATA[Aperiodic Optimal Linear Estimation for Networked Systems With Communication Uncertainties]]>478225622651454<![CDATA[Transmission-Dependent Fault Detection and Isolation Strategy for Networked Systems Under Finite Capacity Channels]]>478226622781018<![CDATA[Observer-Based Non-PDC Control for Networked T–S Fuzzy Systems With an Event-Triggered Communication]]>a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.]]>47822792287787<![CDATA[Task-Space Synchronization of Networked Mechanical Systems With Uncertain Parameters and Communication Delays]]>478228822981062<![CDATA[Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems]]>478229923111384<![CDATA[Stability Analysis of Networked Control Systems With Aperiodic Sampling and Time-Varying Delay]]>478231223201261<![CDATA[Distributed Robust Optimization in Networked System]]>47823212333504<![CDATA[A Layered Event-Triggered Consensus Scheme]]>47823342340635<![CDATA[IEEE Transactions on Cybernetics]]>478C3C3206<![CDATA[IEEE Transactions on Cybernetics]]>478C4C456