<![CDATA[ IEEE/CAA Journal of Automatica Sinica - new TOC ]]>
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TOC Alert for Publication# 6570654 2022August 04<![CDATA[Front cover]]>98c1c211363<![CDATA[Inside front cover]]>98c3c354<![CDATA[Visuals to Text: A Comprehensive Review on Automatic Image Captioning]]>981339136555289<![CDATA[Networked Knowledge and Complex Networks: An Engineering View]]>98136613831362<![CDATA[Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View]]>981384140510533<![CDATA[Complex-Valued Neural Networks: A Comprehensive Survey]]>98140614261463<![CDATA[Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction]]>98142714394108<![CDATA[Consensus Control of Multi-Agent Systems Using Fault-Estimation-in-the-Loop: Dynamic Event-Triggered Case]]>$l_{2}-l_{infty}$ constraint by employing the variance analysis and the Lyapunov stability approaches. Furthermore, the desired estimator and controller gains are obtained in light of the solution to an algebraic matrix equation and a linear matrix inequality in a recursive way, respectively. Finally, a simulation result is employed to verify the usefulness of the proposed design framework.]]>98144014511676<![CDATA[Gradient-Based Differential <tex>$ktext{WTA}$</tex> Network With Application to Competitive Coordination of Multiple Robots]]>$(ktext{WTA})$ operation, this paper proposes a gradient-based differential $ktext{WTA}$ (GD-$ktext{WTA}$) network. After obtaining the network, theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA network. Then, numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ones. Finally, the GD-kWTA network, backed with a consensus filter, is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination, thereby further demonstrating its effectiveness and feasibility.]]>98145214636744<![CDATA[Maneuvering Angle Rigid Formations With Global Convergence Guarantees]]>$Z$ direction. Then, a formation maneuvering law is proposed for the followers to globally maneuver $Z$-weakly angle rigid formations in 3D. The extension to Lagrangian agent dynamics and the construction of the desired rigid formations by using the minimum number of angle constraints are also discussed. Simulation examples are provided to validate the effectiveness of the proposed algorithms.]]>98146414752429<![CDATA[A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems]]>98147614892098<![CDATA[A Novel PDF Shape Control Approach for Nonlinear Stochastic Systems]]>98149014981205<![CDATA[A PD-Type State-Dependent Riccati Equation With Iterative Learning Augmentation for Mechanical Systems]]>$mathrm{K}(mathrm{x})$ for the control input law $mathrm{u}=-mathrm{R}^{-1}(mathrm{x})mathrm{B}^{T}(mathrm{x})mathrm{K}(mathrm{x})mathrm{x}$. The sub-blocks of the overall gain $mathrm{R}^{-1}(mathrm{x})mathrm{B}^{T}(mathrm{x})mathrm{K}(mathrm{x})$, are not necessarily symmetric positive definite. A new design is proposed to transform the optimal gain into two symmetric-positive-definite gains like PD-type controllers as $mathrm{u}= -mathrm{K}_{mathrm{SP}}(mathrm{x})mathrm{e}-mathrm{K}_{mathrm{SD}}(mathrm{x})dot{mathrm{e}}$. The new form allows us to analytically prove the stability of the proposed learning-based controller for mechanical systems; and presents guaranteed uniform boundedness in finite-time between learning loops. The symmetric PD-type controller is also developed for the state-dependent differential Riccati equation (SDDRE) to manipulate the final time. The SDDRE expresses a differential equation with a final boundary condition, which imposes a constraint on time that could be used for finite-time control. So, the availability of PD-type finite-time control is an asset for enhancing the conventional classical linear controllers with this tool. The learning rules benefit from the gradient descent method for both regulation and tracking cases. On-
of the advantages of this approach is a guaranteed-stability even from the first loop of learning. A mechanical manipulator, as an illustrative example, was simulated for both regulation and tracking problems. Successful experimental validation was done to show the capability of the system in practice by the implementation of the proposed method on a variable-pitch rotor benchmark.]]>98149915112060<![CDATA[Secure Bipartite Tracking Control for Linear Leader-Following Multiagent Systems Under Denial-of-Service Attacks]]>9815121515680<![CDATA[Finite-Time Stabilization of Linear Systems With Input Constraints by Event-Triggered Control]]>9815161519440<![CDATA[Exploring the Effectiveness of Gesture Interaction in Driver Assistance Systems via Virtual Reality]]>98152015233555<![CDATA[Domain Adaptive Semantic Segmentation via Entropy-Ranking and Uncertain Learning-Based Self-Training]]>98152415276655<![CDATA[Glioma Segmentation-Oriented Multi-Modal MR Image Fusion With Adversarial Learning]]>98152815312343<![CDATA[Multi-Attention Fusion and Fine-Grained Alignment for Bidirectional Image-Sentence Retrieval in Remote Sensing]]>98153215351805<![CDATA[Estimation Based Adaptive Constraint Control for a Class of Coupled String Systems]]>98153615391915<![CDATA[Battery Full Life Cycle Management and Health Prognosis Based on Cloud Service and Broad Learning]]>98154015422757<![CDATA[Inside back cover]]>98c4c445<![CDATA[Back cover]]>98c5c5362