This paper proposes an attitude tracking control scheme for fixed-wing unmanned aerial vehicles with state, input amplitude and rate constraints, which is able to guarantee the prescribed-time prescribed performance. Firstly, a novel modified prescribed-time performance function is proposed to balance input and performance constraints, with its boundary adaptively adjusted according to the input saturation errors. Secondly, considering the input saturation that occurs during actual flight, an error transformation function and a hyperbolic tangent function are employed to address input amplitude and rate saturation, which prevents abrupt variations in input amplitude. Thirdly, a parameter adaptive estimation method is adopted to approximate the upper bound of external disturbances. On this basis, a back-stepping control method is investigated to guarantee that tracking errors meet the performance boundary within prescribed time under multiple constraints. Comparative simulations illustrate the effectiveness and merits of the proposed control scheme.

This paper presents the dynamic hierarchical attention graph neural network (DHA-GNN), an innovative framework designed for real-time task allocation in UAV swarms navigating complex, dynamic environments, such as urban cityscapes and rugged mountainous terrains. DHA-GNN integrates hierarchical feature aggregation with adaptive multi-level attention mechanisms to model evolving graph structures, capturing real-time interactions among heterogeneous UAVs, tasks, and environmental dynamics. The methodology employs self-learning graph representations to optimize task prioritization and resource allocation under constraints like dynamic obstacles, communication disruptions, and shifting mission priorities. Extensive simulations across diverse scenarios, including industrial facilities, forested regions, and counter-UAV operations, demonstrate that DHA-GNN achieves a task allocation accuracy of 98.7%, outperforming traditional methods by 15% in efficiency and reducing decision latency to 0.0023 milliseconds. It excels in urban (98.2% accuracy) and rugged terrains (97.6% accuracy), ensuring robust adaptability. These results establish DHA-GNN as a leading solution for UAV swarm intelligence, significantly enhancing operational efficiency in disaster response, surveillance, and military applications.

In this article, the adaptive fuzzy predefined-time (PT) consensus is explored for the second-order switched multi-agent system (MAS) with input saturation and uncertainties. The proposed method ensures that the consensus error achieves a user-defined accuracy within a predefined time. To address input saturation, a PT auxiliary signal is devised to adaptively estimate and compensate for the saturation approximation error, enabling the consensus controller to achieve both anti-saturation and PT tracking performance. In addition, fuzzy logic systems and adaptive laws are adopted to approximate unknown system dynamics. By employing the multiple Lyapunov function method, conditions on switching signals are derived to ensure the boundedness of all closed-loop signals within a predefined time. Finally, simulations are conducted to verify the effectiveness and merits of the developed control strategy.

A time-varying formation tracking (TVFT) control method under denial-of-service (DoS) attacks is proposed for a class of multi-uncrewed aerial vehicle (UAV) system with unknown nonlinearity. The neural network approximation is utilized to mitigate the impact of nonlinear dynamics on the tracking performance. Meanwhile, the switching topologies are adopted to ensure the reliability of the communication topology. Considering the resource constraints of the system communication network, an improved integral event-triggered mechanism is devised to further decrease the triggering frequency in comparison to the traditional event-triggered one. By using the multi-Lyapunov function, the tracking error is proved to be bounded. Simulations convincingly demonstrate the effectiveness and merits of the developed TVFT and the integral event-triggered method. Note to Practitioners—Multi-UAV systems are widely utilized in both civil and military domains, with communication networks serving as crucial channels for information exchange among UAVs. However, these communication networks are often vulnerable to various types of attacks. Therefore, the control problems of multi-UAV systems under DoS attacks are of great importance. A switching event-triggered control protocol is developed for multi-UAV systems to facilitate effective formation tracking in the presence of DoS attacks. This protocol employs switching topologies to enhance the reliability and flexibility of the communication network. The improved event-triggered mechanism effectively balances the system performance and the communication consumption.

This paper investigates the state-tracking control problem in conversion mode of a tilt-rotor aircraft with a switching modeling method and a smooth interpolation technique. Based on the nonlinear model of the conversion mode, a switched linear model is developed by using the Jacobian linearization method and designing the switching signal based on the mast angle. Furthermore, an ℋ ∞ state-tracking control scheme is designed to deal with the conversion mode control issue. Moreover, instead of limiting the amplitude of control inputs, a smooth interpolation method is developed to create bumpless performance. Finally, the XV-15 tilt-rotor aircraft is chosen as a prototype to illustrate the effectiveness of this developed control method.

In this article, the problem of event-triggered$\mathcal {H}_{\infty }$control design is investigated for a class of continuous-time switched Takagi–Sugeno (T–S) fuzzy systems. Specifically, the nonweighted$\mathcal {H}_{\infty }$performance is guaranteed for the system with mode-dependent average dwell-time (MDADT) switching, which is more general than the weighted one in most existing results. Meanwhile, the existence of asynchronous phenomenon between event-triggered instants is taken into account, which is more practical and complicated in the system under consideration. It is compulsive in most existing results that the candidate controllers are synchronous with the subsystems, while this constraint is released to deal with the case that the system switches more than once between two adjacent event-trigger instants. First of all, by verifying the existence of the minimal inter-execution time, it is demonstrated that the adopted event-triggered mechanism can exclude the Zeno behavior. Next, an improved criterion on stability and$\mathcal {H}_{\infty }$performance is derived for switched T–S fuzzy systems with MDADT switching. On this basis, the mode-dependent event-triggered mechanism and controllers are co-designed. Finally, two simulations are provided to illustrate the effectiveness of the developed event-triggered control scheme.

In this article, finite-time$\mathcal {H}_{\infty }$control is studied for a kind of continuous-time-switched Takagi–Sugeno (T–S) fuzzy systems with mode-dependent average dwell-time (MDADT) switching. The dynamic event-triggered mechanism (ETM) is utilized to monitor the data transmission from the system plant to the controller, which more efficiently reduces the amount of transmitted data than the conventional static one. First, it is demonstrated that the adopted dynamic ETM can avoid the Zeno behavior, and also yield a larger minimal interexecution time compared with the static one. Then, an improved criterion of finite-time$\mathcal {H}_{\infty }$performance is introduced by utilizing a novel Lyapunov-like function with an internal dynamic variable. Based on this criterion, a dynamic event-triggered controller is designed together with a switching signal subject to the MDADT property. Finally, the validity, and virtues of the proposed control scheme are verified by two simulation examples.

This paper concerns with the stability analysis for a class of discrete-time switched Takagi-Sugeno (T-S) fuzzy systems. By establishing a semitime-dependent Lyapunov function and exploring the property of mode-dependent average dwell-time switching, improved stability criteria are provided for switched T-S fuzzy systems containing both stable and unstable modes. Then, slow and fast switching strategies are designed, which are adopted for stable and unstable subsystems, respectively. Especially, we also provide the stability conditions for switched systems with all modes stable and unstable. Finally, the validities and advantages of provided techniques are illustrated by some simulation examples.

In this paper, asynchronous H∞filtering is studied for discrete-time switched Takagi-Sugeno (T-S) fuzzy systems. New Lyapunov functions are constructed for switched systems with asynchronous switching between subsystems and candidate filters. These improved Lyapunov functions are dependent on the mode of filters instead of subsystems, which are more consistent with the switching mechanism of switched systems with asynchronous switching. Meanwhile, they are quasi-time-dependent, which are effective in achieving more general results than traditional time-independent Lyapunov functions. Based on the new Lyapunov functions, a stability and ℓ2-gain criterion is deduced for switched T-S fuzzy systems. Then, switched fuzzy filters are designed to guarantee that the filtering error system is globally uniformly asymptotically stable and has a prescribed H∞performance. At last, an illustrative example is provided to demonstrate the potentials and advantages of the proposed filtering scheme.

The H∞filter design for a class of switched Takagi-Sugeno (T-S) fuzzy systems with persistent dwell time (PDT) is investigated in this paper. The considered switched fuzzy systems contain a limited number of subsystems and each local subsystem is represented by the well-known T-S fuzzy model. Compared with the dwell time (DT) switching or average DT switching that attracted quantities of interests over the last decade, the PDT switching considered in this paper is known to be more general. The stability and £2-gain analysis for switched systems with PDT switching are derived first, based on which a set of full-order H∞filter is designed to guarantee the global uniform asymptotic stability with a prescribed non-weighted H∞noise attenuation performance for the resulting filtering error system. Finally, the effectiveness of the provided method is illustrated with an example.

This work is concerned with dynamic output feedback control for a class of switched systems with mode-dependent average dwell time switching. By constructing a quasi-time-dependent Lyapunov function, the issues of global uniform exponential stability and ℓ2-gain analysis for the switched system are addressed firstly. Then a set of reduced-order output feedback controllers is designed, which is both mode-dependent and quasi-time-dependent. An example is presented to verify the effectiveness of the proposed method.

For the purpose of improving the predictive accuracy and interpret ability of churn prediction model, TreeLogit model, which integrates the advantage of AD tree model and logistic regression model, is proposed in this paper to predict customers' churn propensities. Compared with TreeNetreg, a model which won the gold prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament) , the overall predictive accuracy of TreeLogit model is not inferior. In fact, the predictive accuracy of TreeLogit is superior to the one of TreeNetreg on two important observation points (i.e. the captured rates in 10% and 50% of the most likely churn groups respectively)