The stochastic generalized Burgers–KdV equations (SGB–KdVEs) arise in the modeling of nonlinear wave phenomena where dispersive, diffusive, and convective effects coexist under stochastic influences. Unlike Brownian-driven models, the incorporation of Lévy noise captures abrupt, non-Gaussian perturbations that more accurately represent realistic wave dynamics. While related studies have mainly focused on Brownian-driven models, the stabilization of SGB–KdVEs with Lévy noise under nonlinear boundary control has not been systematically investigated. To address this gap, we construct a Lyapunov functional and develop a nonlinear mixed boundary controller, from which explicit sufficient conditions are derived to guarantee asymptotic mean-square stability in the presence of stochastic disturbances. To cope with parameter uncertainties, a robust boundary control strategy is proposed, and for systems subject to external perturbations, an H∞ boundary control scheme is further developed to achieve both stability and disturbance attenuation. In contrast to backstepping-based approaches, the proposed method avoids solving kernel equations and offers greater flexibility in handling higher-order nonlinearities and jump disturbances. The theoretical analysis elucidates the coupled effects of noise characteristics, nonlinear terms, and boundary feedback on the evolution of wave energy. Numerical simulations, including scenarios inspired by extreme wave events, validate the theoretical results and demonstrate the effectiveness of the proposed control schemes. Overall, the results establish explicit and verifiable conditions for the boundary control of Lévy-driven stochastic PDEs, ensuring both stability and robust performance.

This paper investigates the inverse optimal control problems for continuous-time linear quadratic regulators over finite-time horizons, aiming to reconstruct the control, state, and terminal cost matrices in the objective function from observed optimal inputs. Previous studies have mainly explored the recovery of state cost matrices under the assumptions that the system is controllable and the control cost matrix is given. Motivated by various applications in which the control cost matrix is unknown and needs to be identified, the authors present two reconstruction methods. The first exploits the full trajectory of the feedback matrix and establishes the necessary and sufficient condition for unique recovery. To further reduce the computational complexity, the second method utilizes the feedback matrix at some time points, where sufficient conditions for uniqueness are provided. Moreover, the authors study the recovery of the state and terminal cost matrices in a more general manner. Unlike prior works that assume system controllability, the authors analyse its impact on well-posedness, and derive expressions for unknown matrices for both controllable and uncontrollable cases. Finally, the authors characterize the structural connection between the inverse problems with the control cost matrix either to be reconstructed or given as a prior.

The pursuit-evasion problem is widespread in nature, engineering, and societal applications. It is commonly observed in nature that predators often exhibit faster speeds than their prey but have less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient strategies for pursuit and evasion. In this paper, we provide a dynamic framework for the pursuit-evasion problem of unicycle systems, drawing inspiration from nature. First, we address the scenario with one pursuer and one evader by proposing an Alert-Turn control strategy, which consists of two efficient ingredients: a sudden turning maneuver and an alert condition for starting and maintaining the maneuver. We present and analyze the escape and capture results at two levels: a lower level of a single run and a higher level with respect to parameters’ changes. In addition, we provide a theorem with sufficient conditions for capture. The Alert-Turn strategy is then extended to more complex scenarios involving multiple pursuers and evaders by integrating aggregation control laws and a target-changing mechanism. By adjusting a ‘selfish parameter’, the aggregation control commands produce various escape patterns of evaders: cooperative mode, selfish mode, and their combinations. The influence of the selfish parameter is quantified, and the target-changing mechanism is explored from a statistical perspective. Our findings align closely with observations in nature. Finally, the proposed strategies are validated through numerical simulations that replicate some chasing behaviors of animals in nature.

This paper proposes a spatial-temporal trajectory planning method for multi-robot systems subject to both motion and safety constraints. By decomposing the planning process into three sequential subproblems: path planning, individual time optimisation, and collision coordination, the proposed method not only reduces the complexity of the overall planning task but also facilitates the incorporation of diverse constraints and optimisation objectives. Specifically, the path planning subproblem generates smooth, length-minimised paths while ensuring adherence to static collision avoidance, boundary position and nonholonomic constraints. Building on these optimised paths, the individual time optimisation subproblem aims to minimise trajectory duration while adhering to dynamic constraints. Then, collision coordination subproblem accounts for collision avoidance constraints between moving robots with minimum makespan. The proposed method is applied to warehouse scenarios and the results indicate that it outperforms baseline methods in terms of planning success rate, computational time, as well as overall makespan.

In the Industrial Internet of Things (IIoT), multi-label classification is challenging due to limited labeled data, class imbalance, and the necessity to consider temporal and spatial dependencies. We propose BiConED, a bi-contrast encoder–decoder self-supervised model integrating two contrasting methods: RAC employs an encoder–decoder with augmented data to capture temporal dependencies and boost information entropy, enhancing generalization under label scarcity. QuadC captures spatial dependencies across channels through convolutions on hidden vectors. Evaluated on the real-world industrial benchmark SKAB, BiConED improves feature extraction for underrepresented classes, achieving a 26% increase in F1 score, a 67.72% reduction in False Alarm Rate (FAR), and a 57.25% decrease in Missed Alarm Rate (MAR) compared to models without the proposed contrasts. Even with limited labeled data, BiConED maintains a FAR below 1% and recovers up to 85% of the F1 score without resampling, demonstrating its robustness in imbalanced IIoT environments.

This paper studies distributed feedback optimization for linear uncertain multiagent systems subject to a class of external disturbances with unknown frequencies. Specifically, the objective is to regulate the outputs of the multiagent system to a common value that minimizes a prescribed global cost function, a sum of local cost functions. A crucial strategy is to develop a distributed optimizer that generates reference signals for the low-level decentralized tracking controllers. For the control synthesis, we use the normal form to derive an internal-model-based control law for the reference tracking and disturbance rejection, and use an adaptive control technique to handle the unknown frequencies of the disturbances. The coupling between the optimizer and the physical control systems is dealt with by a composite Lyapunov function. It is shown that the proposed solution guarantees the boundedness of the closed-loop signals and ensures that the output of each agent converges to the desired minimizer for any initial state. A numerical example demonstrates the effectiveness of the proposed method.

This paper considers the prescribed performance control of unknown multi-input multi-output nonlinear systems with actuator faults. By combining a special funnel function and a barrier function, based on a new coordinate transformation, a low-complexity control approach is proposed not only to guarantee the full-state errors converge into a desired steady-state error boundary in a predefined time, but also to tolerate time-varying actuator faults and achieve the asymptotic tracking, as opposed to the semi-global bounded error tracking results. Our design has a simple structure in the sense that repeatedly taking derivatives of virtual controllers is avoided, and by introducing a time-varying function for the gain design, function approximation methods are also unnecessary. It is applied to the two-degree-of-freedom helicopter system, which demonstrates a good performance in the transient and steady-state stages.

This paper considers online bandit games with arbitrary delays, where the cost functions of all self-interested players are time-varying. In addition, players lack an explicit model of the game and can only learn their actions based on the sole available feedback of delayed cost values. To address this challenging setting, a novel learning algorithm named Cumulative Bandit Online Learning with arbitrary delays (CBOL-ad) is proposed. We conduct regret analysis for time-varying games where the player-specific problem is convex, explicitly revealing the influence of time delays and game structure on the regret bound. In particular, under certain delay conditions, our bound can achieve the same order as that of online bandit optimization problems without delays. Finally, numerical simulations are provided to illustrate the algorithmic performance.

This paper investigates a pattern formation control problem for a multi-agent system modeled with given interaction topology, in which m of the n agents are chosen as leaders and consequently a control signal is added to each of the leaders. These agents interact with each other by Laplacian dynamics on a graph. The pattern formation control problem is formulated as an intrinsic infinite time-horizon linear quadratic optimal control problem, namely, no error information is incorporated in the objective function. Under mild conditions, we show the existence of the optimal control strategy and the convergence to the desired pattern formation. Based on the optimal control strategy, we propose a distributed control strategy to achieve the given pattern. Finally, numerical simulation is given to illustrate theoretical results.