IEEE Transactions on Cybernetics最新文献

Robust optimization (RO) methods have been developed to improve the reliable operation performance of wastewater treatment process (WWTP) under uncertainties. However, the time-linkage uncertainty between uncertainties in the successive process of WWTP leads to a more complex problem. To solve this issue, a distribution-prediction-based robust multiobjective optimization (DP-RMO) algorithm is proposed to obtain robust optimal set points, which can enhance the operation stability of WWTP. First, the robust multiobjective optimization (MOO) objectives are established based on adaptive kernel functions. Then, the effluent quality (EQ) and operation cost (OC) objectives with time-linkage uncertainty in WWTP can be dynamically described. Second, a data-driven predictor is designed based on Gaussian process (GP) to obtain the distribution of time-linkage uncertainty. The predictor takes the variations in the robust solution spaces at adjacent moments as input, which can capture the time-linkage uncertainty between uncertainties at different moments. Third, a self-adjustment evolutionary strategy is proposed to optimize the expectation of robust objective functions through the predicted information. The evolutionary parameters are adaptively adjusted according to the discrepancy in evolutionary states, which can obtain robust optimal set points of WWTP. Finally, the proposed DP-RMO algorithm and other comparison algorithms are tested in the benchmark simulation model No. 1 (BSM1) of WWTP. The experimental results show that DP-RMO can reduce the adverse effects of time-linkage uncertainties. Besides, the optimal set points obtained from DP-RMO exhibit better EQ and OC without sacrificing the robustness performance.

In this article, the problem of practical prescribed-time (PT) cooperative path following (CPF) is investigated for underactuated autonomous surface vehicles (ASVs), which are not equipped with velocity sensors and subject to unmodeled dynamics and actuator saturation. First, a practical PT velocity observer (PTVO) is designed to estimate unmeasurable velocity information, which is then employed in the design of the guidance law and controller. At the kinematic level, a cooperative guidance law based on aperiodic intermittent communication is developed for synchronized path following, effectively saving communication resources. At the dynamic level, an aperiodic intermittent controller incorporating neural networks (NNs) is designed to approximate unmodeled dynamics and effectively avoid continuous operation of actuators with input saturation. Meanwhile, the intermittent adaptive law is constructed to estimate the optimal weights of the NNs, thereby reducing their complexity. The closed-loop system is verified to converge to a residual set within a PT interval. Finally, we conduct numerical simulations to demonstrate the effectiveness of the proposed algorithms.

This article studies a multiplayer capture-the-flag (CTF) differential game, where multiple pursuers try to intercept evaders whose objectives are to first reach a flag and then reach a return region. The critical point is that the flag and the return region are half-planes. Our goal is to address the problem of determining the game winner and computing the pursuit winning strategies. By decomposing the complex multiplayer game into many manageable subgames involving multiple pursuers and one evader, we present the strategies under which the pursuers guarantee to win against the evader, regardless of the evader's strategy, with the necessary and sufficient conditions to determine the game winner. We then extend the results to the cases of the flag-staying time and the minimum safe flag position. To reduce the computational burdens, we prove that if multiple pursuers can ensure the pursuit winning against an evader, then at most two pursuers in this coalition are required. Finally, we solve the multiplayer game by evaluating pairwise subgame outcomes for pursuer-evader matchings. Numerical and experimental results are presented to illustrate the theoretical conclusions.

This article focuses on nonconvex distributed composite optimization over time-varying multiagent networks, where each agent possesses a local objective function, composed of a nonconvex and smooth function plus a nonsmooth function. The network aims to minimize the sum of all local functions subject to local set constraints and global nonconvex coupled inequality constraints. The inherent nonconvex and nonlinear characteristics of the objective and constraint functions pose formidable challenges in developing efficient distributed algorithms with convergence guarantees. To tackle this intricate problem, a novel distributed linearized augmented primal-dual algorithm is designed by incorporating distributed tracking and dynamic consensus techniques. It is theoretically shown that, with appropriately chosen parameters, the proposed algorithm can find an $epsilon $ -Karush-Kuhn-Tucker (KKT) point. Specifically, the sequences of average optimality, constraint violation, and complementary slackness measure converge to zero at sublinear rates. Finally, a numerical application is presented to validate the effectiveness of the proposed algorithm.

This article aims to investigate relaxed local stabilization for discrete-time Takagi-Sugeno fuzzy systems with structural relaxation under guaranteed resilience. To mitigate the conservatism and computational burden associated with conventional multiple summation-type approaches for exploiting high-degree membership information, a novel Lyapunov function and nonparallel distributed compensation (non-PDC) control law are developed within an augmented membership-quadratic framework, which relaxes the symmetry constraints on the intertemporal cross terms. To overcome the limitations of existing resilient stabilization methods that rely heavily on a user-defined hyperparameter, a matrix-type threshold condition is introduced, enhancing both practicality and numerical efficiency. Based on orthogonal complements, new structural relaxation lemmas within the membership-quadratic framework are proposed for guaranteeing resilient stabilization. Finally, the effectiveness and reduced conservatism of the proposed method are validated through benchmark examples, demonstrating its computational efficiency and improved performance compared to existing approaches.

Human-gaze-target prediction aims to predict the target point or object that humans are looking at in images. However, existing methods predominantly rely on vision-only features, which often struggle to capture the semantic context of small or occluded objects and lack explicit priors for precise head direction regression, leading to slow convergence and suboptimal performance. Therefore, we introduce VL-HTR, a novel vision-language learning method for human-target representation, which integrates multimodal knowledge from vision-language models (VLMs) to construct robust human-target relationships. Unlike traditional approaches, extracting multimodal features via pretrained VLMs enhances the model's grasp of human-target knowledge through the learnable target class and direction context. Then, a language-guided query alignment (LQA) module is introduced to improve the semantic-aware object representation capability through vision-language query alignment. Finally, to accelerate the gaze point regression learning process, we design a language-guided direction prediction (LDP) module to introduce multimodal human gaze direction priors, thereby facilitating the human-target relationship construction. Extensive validations across two distinct tasks, i.e., gaze object prediction (GOP) and gaze target estimation, involving five challenging benchmarks, demonstrating that VL-HTR achieves superior performance and much faster training convergence.

In this article, the attitude maneuver control with the arbitrary convergence time is investigated for rigid spacecraft. First, a time-varying sliding mode function expressed by a piecewise function is designed by using an exponential function. This designed sliding mode function contains two equilibria of the attitude control systems. Furthermore, an attitude control law is designed with the aid of this new sliding mode function such that the states of the closed-loop attitude system remain on the sliding mode surface from the initial time instant, and converge to the origin at an arbitrarily preset time. In addition, the unwinding phenomenon can also be avoided when the proposed control law is used.