This paper presents a new multi-agent planning framework under temporal logic constraints, which extends the Conflict-Based Search (CBS) approach to handle linear temporal logic (LTL) tasks. First, an algorithm is developed that integrates the Bottleneck Assignment Problem (BAP) algorithm to optimize task allocation. By verifying and adjusting task sequences based on relaxed partially ordered set structures, the proposed method ensures feasible and optimal plans. Experiments demonstrate that our algorithm can effectively minimize the total task completion time (makespan) while satisfying complex temporal constraints, making it a promising solution for multi-agent planning problems.

As offshore wind power progresses into deeper waters, the safety and reliability of floating wind turbines in complex marine environments encounter significant challenges. This paper proposes a coupled dynamic response simulation method that integrates Computational Fluid Dynamics (CFD) with the catenary method for a novel floating stepped wind turbine platform. By combining numerical simulations with model testing, we systematically investigate the dynamic characteristics of both the floating platform and the mooring system. The results indicate that the catenary method demonstrates high predictive reliability for mooring tension (with an average accuracy of 85.8%) and platform motion response (with an average accuracy of 82.5%) under regular wave conditions. Further simulations conducted for extreme sea conditions, categorized as Class V sea state (wave height of 5 m and period of 10 s) and Class VI sea state (wave height of 6 m and period of 12 s), reveal that the heave amplitude approaches the safety threshold, posing a risk of wave impact. The mooring line on the windward side (Line 1) becomes a weak link in the system due to dynamic overload, with its tension significantly exceeding that of the leeward side, potentially leading to fatigue failure. This study provides a theoretical foundation for the safe design and optimization of floating wind turbines by constructing a high-precision coupled model, completing experimental validation, and elucidating response patterns under extreme sea conditions.

Populations that are encountered in ecology, epidemics, or biotechnology, do not only interact over time but also age over time. They are modeled as age-structured partial differential equations, where age is the space variable. Involving integrals over age, they are in fact integro-partial differential equations. Population dynamics can be observed in chemostat reactors, where harvesting is used as input to regulate population densities to track desired reference trajectories. Up to date, such models have only been considered as single input, single output systems, imposing strict constraints on reference trajectories when multiple species are involved. We present a novel model for two competing populations in a cascaded chemostat setup, allowing for an independent tracking control of each species. The control is implemented in a two degree of freedom structure based on the linearized system, with an inversion-based feedforward control and a robust feedback law. The designed control is able to track sinusoidal reference trajectories around the steady state with low error, underlining the great potential of model-based control in the chemostats.

In increasingly complex offshore operations, accurate identification of hydrodynamic parameters is critical for high-precision ship motion modeling and plays a vital role in ensuring maritime safety. To address this challenge, a Variable Forgetting Factor Multi-Innovation Recursive Least Squares (VFF-MIRLS) algorithm is proposed for parameter identification. To mitigate the issue of parameter drift commonly observed in ship systems, an improved PID-based Search Algorithm (IPSA) is introduced to optimize the identification results. The effectiveness of the optimization strategy is validated using a standard benchmark test set, and the superior performance of the proposed method is further confirmed through ship maneuverability prediction simulations. The results demonstrate that the proposed approach can reliably estimate hydrodynamic parameters and accurately capture ship dynamics, providing a promising solution for intelligent control and navigation systems.

Predicting the Remaining Useful Life (RUL) is vital for the Prognostic Health Management (PHM) of turbofan engines. Recently, deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have been applied to RUL prediction. However, these models often struggle to balance the ability to capture long-range dependencies with computational efficiency. To address this, we propose an efficient RUL prediction method named ETSEnet, based on an improved Reformer with multi-channel perception. This model utilizes a hashing bucket strategy for sparse computation, reducing computational resource consumption while capturing long-term dependencies. Additionally, the Squeeze-and-Excitation mechanism quantifies inter-channel correlation and enhances dependency through adaptive feature recalibration, significantly improving prediction accuracy and convergence speed. Experiments on the NASA C-MAPSS dataset demonstrate that our model surpasses others in both accuracy and training time.

This paper introduces an enhanced zonotope reduction method to address the trade-off between computational efficiency and precision in uncertain dynamical systems, a step forward in the management of conservatism. Zonotopes are widely used for bounded uncertainty representation due to their favorable algebraic properties, but their complexity escalates with the number of generators, requiring effective reduction strategies. Here, an improved approach based on the Hyperplan Zonotopic Contractor (HZC) reduction method is introduced, which consistently achieves higher accuracy with fewer generators. Unlike interval-based methods, this approach operates directly on zonotopes, preserving their geometric properties while optimizing computational performance. Various cases simulations demonstrate the efficiency of the proposed method, particularly for generator counts within practical ranges. The preliminary results are promising and suggest that the proposed method could serve as a foundation for future advances in precise and scalable zonotope reduction techniques.

This study proposes a distributed adaptive nonsingular terminal sliding mode iterative learning control method based on a linear extended state observer (LESO) for high-speed train speed tracking, targeting challenges of iterative initial state deviations and complex disturbance suppression. Utilizing trains’ repetitive operational patterns, the approach introduces a time-varying nonsingular terminal sliding mode surface to remove traditional initial state constraints and employs LESO for real-time estimation and dynamic compensation of multi-source disturbances. The framework integrates an adaptive iterative update law for online observation error estimation with a fully saturated adaptive control law to enforce input constraints. Theoretical stability is established through a composite energy function analysis, rigorously proving differential negative definiteness in the iteration domain and signal boundedness. The designed sliding mode surface exhibits asymptotic convergence, with finite-time tracking error convergence validated within the surface. Simulations demonstrate the algorithm’s ability to maintain high-precision speed tracking under iterative initial errors and compound disturbances.