查看更多>>摘要：This article studies the adaptive prescribed-time fuzzy optimal containment control issue for multiagent systems (MASs) with deferred output constraints based on the reinforcement learning (RL) algorithm. Given that agents require confidential state messages, an output mask scheme is delicately synthesized to ensure that other agents cannot identify the true state message, potentially adding to the sophistication of the containment control process of MAS. Then, an adaptive prescribed-time fuzzy optimal containment control strategy is developed that counts on the masked state of neighboring agents. In addition, an auxiliary error via the shifting function is incorporated into the nonlinear mapping function to manage error constraints, not only avoiding the feasibility criteria but also realizing the unified control. Notably, an emerging intermediate variable is executed to tackle the issue of unknown control gains acting on the RL-based recursive design procedure. Moreover, the drawback of semiglobal boundedness of the error surface induced by dynamic surface control can be avoided with the aid of the novel Lyapunov-like energy candidate. With the assistance of the practical prescribed-time stability, it can be guaranteed that the original state value of each agent remains undisclosed, and the output of the followers can be centered on a convex hull made up of leaders within a prescribed time. Herein, the efficacy of the suggested tactic is exemplified through two illustrative examples.
查看更多>>摘要：Complex systems are frequently influenced by uncertain factors, making it difficult for traditional fixed-model control schemes to achieve high-precision control. Data-driven control methods offer a solution, but they face challenges in constructing accurate models due to insufficient excitation in operational data. Moreover, mismatches between historical models and new conditions coupled with limited data accumulation under new conditions reduces the operational performance throughout the entire process. To address these issues, this paper proposes a generalized integrated fuzzy model predictive control (GIF-MPC) framework. It combines the generalization capability of fuzzy control with the precision of model predictive control to ensure highprecision control under all conditions. Specifically, a strategy switching mechanism, triggered by a mismatch characteristic parameter is first proposed, which transitions the original strategy to a fuzzy-driven excitation control method, thereby mitigating the control performance degradation caused by the mismatch between control strategies and complex systems. Then, a fuzzy control feature extraction method is proposed to balance fuzzy set activation and improve adaptability to unknown conditions. Additionally, an optimal input excitation design method is proposed to tackle insufficient data excitation, enabling effective control. Once sufficient data is accumulated, the model switches to model predictive control. The dual decision mechanism guided by the data information and triggered by the mismatch characteristic parameter effectively ensures high precision control under uncertainties. Numerical experiments demonstrate that the GIF-MPC method ensures high-precision control throughout disturbances and condition changes. The solution is also successfully deployed in an industrial setting, validating its excellent control performance under full operation conditions.
查看更多>>摘要：Automatic acne severity grading is crucial for the accurate diagnosis and effective treatment of skin diseases. However, the acne severity grading process is often ambiguous due to the similar appearance of acne with close severity, making it challenging to achieve reliable acne severity grading. Following the idea of fuzzy logic for handling uncertainty in decision-making, we transforms the acne severity grading task into a fuzzy label learning (FLL) problem, and propose a novel adjacency-aware fuzzy label learning (AFLL) framework to handle uncertainties in this task. The AFLL framework makes four significant contributions, each demonstrated to be highly effective in extensive experiments. First, we introduce a novel adjacency-aware decision sequence generation method that enhances sequence tree construction by reducing bias and improving discriminative power. Second, we present a consistency-guided decision sequence prediction method that mitigates error propagation in hierarchical decision-making through a novel selective masking decision strategy. Third, our proposed sequential conjoint distribution loss innovatively captures the differences for both high and low fuzzy memberships across the entire fuzzy label set while modeling the internal temporal order among different acne severity labels with a cumulative distribution, leading to substantial improvements in FLL. Fourth, to the best of our knowledge, AFLL is the first approach to explicitly address the challenge of distinguishing adjacent categories in acne severity grading tasks. Experimental results on the public ACNE04 dataset demonstrate that AFLL significantly outperforms existing methods, establishing a new state-of-the-art in acne severity grading.
查看更多>>摘要：This article proposes a quantum group decision model that integrates intuitionistic fuzzy sets to represent the uncertainty of meteorological disaster information, addressing both vagueness and probabilistic uncertainty. This makes it particularly suitable for modeling the complex and dynamic decision-making processes during emergency responses. The model employs regret theory for attribute weight determination and constructs a quantum-like Bayesian network (QLBN), where Deng entropy is applied to measure the mutual interference effects among decision-makers. Decision-makers' weights are determined using grey relational analysis and incorporated as the initial layer in the Bayesian network. The conditional probabilities within the QLBN are derived by integrating attribute weights and regret utility functions, and the alternatives are ranked based on their final quantum probabilities. The effectiveness and stability of the model are demonstrated through its application in emergency alternative selection for meteorological disasters, confirmed by sensitivity and comparison analyzes.
查看更多>>摘要：In this article, a time-varying group formation-containment tracking problem for nonlinear multiagent systems (NMASs) with intermittent actuator faults is considered. The NMASs are composed of multiple subgroups responsible for different target tracking, and they are interconnected through a weighted digraph. The unknown nonlinear dynamics of nonstrict-feedback NMASs are approximated by fuzzy-logic systems, and the adaptive backstepping is employed to design the virtual controllers along with their adaptive parameters. Then, two novel fault-tolerant controllers are designed for the formation leaders and followers to achieve group formation tracking and containment control, even in the presence of intermittent actuator faults. Both controllers are fully distributed as only neighbor information is required instead of global information. Finally, simulation experiments for multiple unmanned surface vehicles are provided.
查看更多>>摘要：This article investigates the event-triggered trajectory tracking control for unmanned surface vessels (USVs) with prescribed performance subjected to asymmetric time-varying state constraints. In order to realize the prescribed-time control task, a performance function is introduced and the fuzzy logic systems are adopted to estimate the unknown nonlinearities of the USVs model. Subsequently, a flexible event-triggered mechanism is designed to minimize the wastage of communication resource, taking into account the continuous updating of the actual control input. A practical virtual control signal is devised and used for backstepping design. On this basis, an event-triggered adaptive fuzzy trajectory tracking control algorithm with prescribed performance is developed by combining the defined performance function and the barrier Lyapunov function approach, which not only solves the asymmetric time-varying state constraints of the USVs, but also ensures that the USVs achieve the predetermined tracking performance, meanwhile, all the closed-loop signals can be maintained bounded. Finally, the simulation results are performed to demonstrate the feasibility of the developed control scheme.
查看更多>>摘要：This article addresses the secure control problem for the Takagi–Sugeno (T–S) fuzzy wind turbine system (WTS) subject to hybrid cyberattacks. To reduce system performance loss and redundant data transmission under such attacks, a novel adaptive memory event-triggered mechanism (ETM) is proposed, offering two key advantages. First, unlike traditional ETMs that rely solely on current system information, the adaptive memory ETM utilizes historical release data to adjust communication frequency and enhance control effectiveness. Second, an attack-related triggering condition and adaptive law are introduced to reduce the abnormaldata transmission induced by denial of service (DoS) attacks and extend the lifespan of the sensor node. Sufficient conditions are derived to ensure the mean-square $\mathcal {H}_{\infty }$ asymptotic stability of the resulting T–S fuzzy WTS, while also guaranteeing uniformly ultimate boundedness in the presence of DoS attacks. Finally, an illustrative example is used to show the effectiveness of the proposed method.
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