<![CDATA[ IEEE Transactions on Systems, Man, and Cybernetics: Systems - new TOC ]]>
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TOC Alert for Publication# 6221021 2021March 04<![CDATA[Table of contents]]>513C11715448<![CDATA[IEEE Transactions on Systems, Man, and Cybernetics publication information]]>513C2C2109<![CDATA[Inverse Engineering Preferences in the Graph Model for Conflict Resolution]]>51317161724785<![CDATA[Asynchronous Finite-Time Filtering of Markov Jump Nonlinear Systems and Its Applications]]>513172517341229<![CDATA[Asymmetric Bounded Neural Control for an Uncertain Robot by State Feedback and Output Feedback]]>${n}$ -link rigid robotic manipulator with unknown dynamics. With the combination of the neural approximation and backstepping technique, an adaptive neural network control policy is developed to guarantee the tracking performance of the robot. Different from the existing results, the bounds of the designed controller are known a priori, and they are determined by controller gains, making them applicable within actuator limitations. Furthermore, the designed controller is also able to compensate the effect of unknown robotic dynamics. Via the Lyapunov stability theory, it can be proved that all the signals are uniformly ultimately bounded. Simulations are carried out to verify the effectiveness of the proposed scheme.]]>513173517462520<![CDATA[Wrapper Framework for Test-Cost-Sensitive Feature Selection]]>513174717562552<![CDATA[The Role of Reverse Edges on Consensus Performance of Chain Networks]]>513175717652510<![CDATA[Decentralized Event-Triggered Tracking of a Class of Uncertain Interconnected Nonlinear Systems Using Minimal Function Approximators]]>513176617781759<![CDATA[Toward Optimal Risk-Averse Configuration for HESS With CGANs-Based PV Scenario Generation]]>513177917934371<![CDATA[Memristive LSTM Network for Sentiment Analysis]]>513179418042359<![CDATA[A Double-Blind Anonymous Evaluation-Based Trust Model in Cloud Computing Environments]]>513180518161412<![CDATA[Hybrid PD-Fuzzy and PD Controllers for Trajectory Tracking of a Quadrotor Unmanned Aerial Vehicle: Autopilot Designs and Real-Time Flight Tests]]>nonlinear control system, comprising of a conventional proportional-differential (PD) controller and a PD-type fuzzy logic autopilot for the trajectory tracking of a quadcopter drone. Given the inherent nature of traditional control, which is model-based, and the essence of fuzzy logic control, which is knowledge-based, the proposed hybrid controllers can provide a more robust solution in the face of uncertainties. Both controllers operate in a parallel incremental form to improve the transient performance and the robustness of the closed-loop control system. Through extensive computer simulations supported by real-time flight tests, this paper highlights the efficacy of the proposed hybrid control system in the presence of some parameter variations, nonlinear aerodynamic models, and some external disturbances (e.g., wind gusts). The Dryden and 1–cos turbulence models are employed to represent the effects of wind gusts under realistic flight environments. The stability analysis of the closed-loop control system is conducted using Lyapunov’s indirect method.]]>513181718294384<![CDATA[Concurrent Processing Cluster Design to Empower Simultaneous Prediction for Hundreds of Vessels’ Trajectories in Near Real-Time]]>513183018433554<![CDATA[Reliability Modeling for Sparsely Connected Homogeneous Multistate Consecutive-k-Out-of-n: GSystems]]>${k}$ -out-of-${n}$ : ${G}$ system. The proposed model is regarded as a natural extension of the consecutive-${k}$ -out-of-${n}$ system. Moreover, the proposed system model could cover three special MS submodels, i.e., a decreasing MS: ${G}$ system model, an increasing MS: ${G}$ system model, and a nonmonotonic MS: ${G}$ system model. Three corresponding real-world examples are given to illustrate the applications of the model. Meanwhile, by utilizing the well-known finite Markov chain imbedding method, the explicit system reliability and state distribution for the three kinds of submodels are presented, respectively. Finally, the developed indexes and analysis method are demonstrated by numerical examples.]]>513184418541202<![CDATA[Fuzzy Grey Choquet Integral for Evaluation of Multicriteria Decision Making Problems With Interactive and Qualitative Indices]]>$ {lambda }$ -fuzzy-measures following the weights given by experts in order to enhance the consistency of weights. Then the correlation coefficients are aggregated through Choquet integral among ${lambda }$ -fuzzy-measures, which can reflect interactions among indices. In addition, according to the characteristics of ${lambda }$ -fuzzy-measures, the construction guidelines for a corresponding index system are given to overcome the limitations of FGCI. Finally, the performance of the proposed method is demonstrated via a practical example of green design evaluation and compared with the GCE method. The results validate its feasibility and effectiveness.]]>513185518682505<![CDATA[Robust Fixed-Time Consensus Tracking Control of High-Order Multiple Nonholonomic Systems]]>513186918803394<![CDATA[Extended State Observer-Based Data-Driven Iterative Learning Control for Permanent Magnet Linear Motor With Initial Shifts and Disturbances]]>513188118911100<![CDATA[Partial-Nodes-Based Scalable H<sub>∞</sub>-Consensus Filtering With Censored Measurements Over Sensor Networks]]>${H} _{{infty }}$ -consensus filtering problem for a class of discrete time-varying systems subject to multiplicative noises and censored measurements over sensor networks (SNs). For the underlying SN, it is assumed that only the measurement outputs from partial sensor nodes are available. Also, the phenomenon of censored measurements is taken into account to reflect the limited capability in measuring. A new ${H} _{{infty }}$ -consensus performance index is put forward to evaluate the disturbance rejection level of the filters against the simultaneous presence of external disturbances, initial conditions, as well as censoring effects. By utilizing the vector dissipativity theory and the recursive matrix inequality technique, sufficient conditions are established under which the prescribed ${H} _{{infty }}$ -consensus performance index is achieved. The parameters of the desired distributed filters are calculated via solving certain matrix inequalities, where such a calculation is conducted in a local sense so as to preserve the scalability of the filter design. Finally, a numerical simulation example is provided to demonstrate the validity and applicability of the proposed filtering strategy.]]>51318921903856<![CDATA[Full Information Estimation for Time-Varying Systems Subject to Round-Robin Scheduling: A Recursive Filter Approach]]>513190419161103<![CDATA[Adaptive Online Learning With Regularized Kernel for One-Class Classification]]>513191719322042<![CDATA[Modeling a Decision-Maker’s Choice Behavior Through Perceived Values]]>513193319441166<![CDATA[Command Filter-Based Adaptive Fuzzy Control for Nonlinear Systems With Unknown Control Directions]]>51319451953689<![CDATA[Quasi-Synchronization of Delayed Memristive Neural Networks via a Hybrid Impulsive Control]]>513195419652334<![CDATA[Robust Exponential Synchronization for Memristor Neural Networks With Nonidentical Characteristics by Pinning Control]]>513196619801618<![CDATA[Interval Type-2 FNN-Based Quantized Tracking Control for Hypersonic Flight Vehicles With Prescribed Performance]]>513198119931981<![CDATA[Strict Lyapunov Functions for Homogeneous Time-Varying Systems]]>51319942002427<![CDATA[Zero-Shot Learning Based on Multitask Extended Attribute Groups]]>513200320111747<![CDATA[Quantized Sliding Mode Control of Unmanned Marine Vehicles: Various Thruster Faults Tolerated With a Unified Model]]>513201220263360<![CDATA[Stability Criteria for Impulsive Stochastic Functional Differential Systems With Distributed-Delay Dependent Impulsive Effects]]>51320272032373<![CDATA[TechRxiv: Share Your Preprint Research with the World!]]>51320332033369<![CDATA[Introducing IEEE Collabratec]]>513203420342132<![CDATA[IEEE Systems, Man, and Cybernetics Society Information]]>513C3C3104<![CDATA[Information For Authors]]>513C4C4218