Multiple Simulations Research Articles

A New Methodology for Optimization of Lashing Lines in the Securing Arrangement of Non-Standardized Cargo on Ships

The stowage plan and the securing arrangement of non-standardized cargo are some of the most important aspects in terms of cargo safety and economic costs. For this reason, the optimization of this operation is crucial and a daily challenge for securing planners trying to fulfill both requirements. In the present paper, a new methodological optimization is proposed presenting novel mathematical models and new 3D maps useful for the people in charge of stowage and securing arrangement. For this goal, the materials followed were the Code of Safe Practice for Cargo Stowage and Securing (CSS) of the International Maritime Organization (IMO) because many ships’ Cargo Securing Manuals refer to this international standard. Using an initial case study and making use of response surface techniques of design of experiments (DOE), multiple numerical simulations were performed to obtain novel mathematical models, revealing a high precision. Moreover, new 3D maps were presented and are very interesting tools due to their ease of understanding. The obtained results were compared with other simulations carried out where different variables were employed. The presented models of this methodology can predict the best securing arrangement to fulfill the balance forces in any stowage position (vertical and longitudinal) using the minimum securing devices and keeping the standards of safety. These methodological tools offer valuable advice to the shipping industry with responsibilities involved in the securing design of non-standardized cargo items.

Evaluation of automated driving safety in urban mixed traffic environments

AbstractConflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi‐agent driving simulator, which allows real‐time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict‐related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established.

Enhancing livelihood resilience through hybrid ecological compensation: evidence from Potatso National Park, China

Abstract Balancing biodiversity conservation and the socioeconomic well-being of ethnic minority communities presents a significant challenge in protected areas (PAs). Ecological compensation (EC) is a crucial policy instrument for improving the livelihood of affected residents, but its application in ethnic minority-dominant regions is complicated by cultural differences and fragile economic foundations. This study evaluates the impact of an innovative hybrid EC mechanism, integrating cash payment, employment support, and education incentive, on the livelihood resilience of ethnic minority groups and identifies the most cost-effective mechanism. Using field data from rural households in Potatso National Park, analyzed through hierarchical multiple regression and scenario simulations within the Sustainable Livelihood Framework, the study finds that: (i) hybrid EC mechanisms significantly improve overall livelihoods, with households receiving employment support and education incentive showing 0.074 and 0.052 higher average livelihood score, respectively (on a 0–1 scale); (ii) different EC types have varied effects across households; and (iii) hybrid EC mechanisms increase social benefits by 6.97%–37.65%, with only a 1.30%–20.15% cost increase compared to baseline scenario. The findings highlight the need for diversified and optimized EC policies to improve livelihood resilience and maximize cost-effectiveness in PAs.

Simulation study of the impacts of E-region density on the growth of equatorial plasma bubbles

Equatorial plasma bubbles (EPBs) in the ionospheric F region are notorious for causing severe scintillation in radio signals, posing significant challenges for communication and navigation systems. Understanding and forecasting EPB occurrence is crucial from a space weather perspective, given their impact on satellite and terrestrial communication. In this study, we present the impacts of E-region conductivity on the generation of EPBs by using the 3D high-resolution bubble (HIRB) model. By changing the production rate of NO+ ions in the E region, the flux-tube-integrated linear growth rate of the Rayleigh–Taylor instability can be modified. Multiple simulation runs show that even a moderate variation of the growth rate turns into a significant difference in EPB growth into the top of the ionosphere. This is a major factor that has made forecasting EPB generation quite difficult for several decades.

Reduced-Order Modeling for Subsurface Flow Simulation in Fractured Reservoirs

Summary Reservoir simulation for fractured reservoirs is often challenging and time-consuming due to the strong heterogeneity and complex flow dynamics introduced by fracture-matrix interactions. In this study, we introduce a novel reduced-order modeling procedure to speed up the flow simulation of fractured reservoirs. The reduced-order model (ROM) is developed based on proper orthogonal decomposition (POD) in conjunction with the embedded discrete fracture model (EDFM) that provides full-order simulation results. With the full-order training simulation, snapshots of reservoir pressure and saturation state at different timesteps are captured and assembled into separate data matrices. Singular value decomposition (SVD) is then applied to these data matrices to obtain a reduced set of orthogonal base vectors for pressure and saturation solutions, respectively. These base vectors enable the projection of high-dimensional linear equations into much lower-dimensional spaces, which significantly accelerates the process of solving nonlinear governing equations under the EDFM approach. The developed reduced-order modeling procedure is implemented in the MATLAB reservoir simulation toolbox (MRST) and tested via multiple cases for both 2D and 3D fractured reservoirs under different boundary and well control scenarios. In certain challenging cases, the use of multiple training simulations is explored and is shown to provide improved predictions. Overall, the proposed ROM approach is able to provide simulation results that are very consistent with those obtained from the full-order simulations while achieving computational speedups of about an order of magnitude for large-scale cases. These observations indicate that the proposed ROM exhibits satisfactory generalization performance, making it suitable for problems that require many flow simulations under different settings, such as production optimization.

Fusion of improved RRT and ant colony optimization for robot path planning

Abstract To address the issues of poor guidance at the beginning of the Ant Colony Optimization (ACO) algorithm, non-smooth paths, and its tendency to fall into local optima, this paper proposes a path planning approach based on the Rapidly-exploring Random Tree (RRT) and Ant Colony Optimization (ACO). Firstly, obstacles are inflated to set a safety distance, and a differentiated pheromone distribution is created using the sub-optimal trajectory produced by the improved RRT, guiding the initial direction of the ant colony. Secondly, dynamic strategies are introduced into the evaporation coefficient and heuristic factor, adjusting their weights according to the number of iterations to enhance the attraction of the target point to the ants. Then, a reward-punishment mechanism is used to update the pheromone, solving the problem of local optima. Finally, a pruning optimization strategy based on the maximum turning angle is employed to remove redundant nodes, making the path smoother. Multiple simulation results confirm that the algorithm possesses good global search capabilities and robustness under various conditions.

A hybrid tau-leap for simulating chemical kinetics with applications to parameter estimation.

We consider the problem of efficiently simulating stochastic models of chemical kinetics. The Gillespie stochastic simulation algorithm (SSA) is often used to simulate these models; however, in many scenarios of interest, the computational cost quickly becomes prohibitive. This is further exacerbated in the Bayesian inference context when estimating parameters of chemical models, as the intractability of the likelihood requires multiple simulations of the underlying system. To deal with issues of computational complexity in this paper, we propose a novel hybrid τ-leap algorithm for simulating well-mixed chemical systems. In particular, the algorithm uses τ-leap when appropriate (high population densities), and SSA when necessary (low population densities, when discrete effects become non-negligible). In the intermediate regime, a combination of the two methods, which uses the properties of the underlying Poisson formulation, is employed. As illustrated through a number of numerical experiments, the hybrid τ offers significant computational savings when compared with SSA without, however, sacrificing the overall accuracy. This feature is particularly welcomed in the Bayesian inference context, as it allows for parameter estimation of stochastic chemical kinetics at reduced computational cost.

Fuzzy RANCOM: A novel approach for modeling uncertainty in decision-making processes

Multi-Criteria Decision Analysis (MCDA) methods are essential for addressing real-world challenges in the dynamic field of decision support technology. As technology advances, the expanding variety of multi-criteria evaluation methods highlights the critical importance of method selection for reliable results. These methods can be applied to scenarios involving both precise data and uncertainties. Expert knowledge is relevant in navigating real-world decision problems, particularly in uncertain environments where judgment inaccuracies can create challenges. Determining criteria weights is a pivotal step, as it significantly influences final outcomes and provides a nuanced understanding of each factor's impact on the evaluation.This paper addresses the pressing need for methods that account for uncertainty and effectively reduce the impact of inaccuracies in expert judgment. To this end, we introduce the Fuzzy RANking COMparison (RANCOM) method, a novel fuzzy subjective weighting approach that extends the advantages of the original RANCOM method to manage uncertainty. Using Triangular Fuzzy Numbers (TFNs), the proposed extension maintains simplicity, rapid execution, and flexibility in establishing criteria relationships. Key contributions of this paper include the development of the Fuzzy RANCOM method and a comparative analysis with the Fuzzy Analytical Hierarchy Process (AHP) method. Through multiple simulations, the study examines the operational dynamics of these methods, offering a valuable tool for decision-makers, especially in contexts where expert judgments may be prone to inaccuracies. The development of the Fuzzy RANCOM method provides a robust solution, equipping decision-makers with an effective tool to determine criteria weights while minimizing the impact of potential judgment errors.

Impact of Uncertainties of Nuclear Severe Accidents on Radiological and Nuclear Dispersion Predictions

Protection of the public as well as the environment is primal task of the regulators and provisions to sustain integrity of the plant have been developed. In addition to that, the consequences of possible severe accidents are necessary to develop strategies to mitigate the impact. Evaluation by using severe accident tools is common practice accepted by regulators, but these tools rely on models that generated by using limited experimental data. Thus, reliable evaluation of the accident also requires uncertainty quantification of the results. In this work, selected accident case on VVER-1000 is performed by using ASTEC tool and the uncertainties of the ASTEC code is quantified by using KATUSA tool with the goal of determination of the potential impact range. 100 samples are generated according to selected uncertain parameters, their probabilistic distribution functions (PDFs) and variation range, and multiple ASTEC code simulations are performed to evaluate the results. Finally, JRODOS calculation is performed on Zaporizhzhia NPP with worst-case and best-estimate scenarios to identify the difference on the radiological impact. The potential difference on the inventories results with almost two times higher radiological contamination of the selected area on selected period which causes almost 1.5 times higher doses on the population.

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

AbstractThis study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

An automated toolbox for microcalcification cluster modeling for mammographic imaging.

Mammographic imaging is essential for breast cancer detection and diagnosis. In addition to masses, calcifications are of concern and the early detection of breast cancer also heavily relies on the correct interpretation of suspicious microcalcification clusters. Even with advances in imaging and the introduction of novel techniques such as digital breast tomosynthesis and contrast-enhanced mammography, a correct interpretation can still be challenging given the subtle nature and large variety of calcifications. Computer simulated lesion models can serve to develop, optimize, or improve imaging techniques. In addition to their use in comparative (virtual clinical trial) detection experiments, these models have potential application in training deep learning models and in the understanding and interpretation of breast lesions. Existing simulation methods, however, often lack the capacity to model the diversity occurring in breast lesions or to generate models relevant for a specific case. This study focuses on clusters of microcalcifications and introduces an automated, flexible toolbox designed to generate microcalcification cluster models customized to specific tasks. The toolbox allows users to control a large number of simulation parameters related to model characteristics such as lesion size, calcification shape, or number of microcalcifications per cluster. This leads to the capability of creating models that range from regular to complex clusters. Based on the input parameters, which are either tuned manually or pre-set for a specific clinical type, different sets of models can be simulated depending on the use case. Two lesion generation methods are described. The first method generates three-dimensional microcalcification clusters models based on geometrical shapes and transformations. The second method creates two-dimensional (2D) microcalcification cluster models for a specific 2D mammographic image. This novel method employs radiomics analysis to account for local textures, ensuring the simulated microcalcification cluster is appropriately integrated within the existing breast tissue. The toolbox is implemented in the Python language and can be conveniently run through a Jupyter Notebook interface, openly accessible at https://gitlab.kuleuven.be/medphysqa/deploy/breast-calcifications. Validation studies performed by radiologists assessed the level of malignancy and realism of clusters tuned with specific parameters and inserted in mammographic images. The flexibility of the toolbox with multiple simulation methods is illustrated, as well as the compatibility with different simulation frameworks and image types. The automation allows for the straightforward and fast generation of diverse microcalcification cluster models. The generated models are most likely applicable for various tasks as they can be configured in a variety of ways and inserted in different types of mammographic images of multiple acquisition systems. Validation studies confirmed the capacity to simulate realistic clusters and capture clinical properties when tuned with appropriate parameter settings. This simulation toolbox offers a flexible means of simulating microcalcification cluster models with potential use in both technical and clinical research in mammography imaging. The 3D generation methods allow for specifying many characteristics regarding the calcification shape and cluster architecture, and the 2D generation method presents a novel manner to create microcalcification clusters tailored to existing breast textures.

A trajectory tracking control method for the discharge arm of the self-propelled forage harvester

The cooperative operation between the self-propelled forage harvester and the trailer aims to achieve precise and automatic forage unloading. The discharge arm serves as the core structure for conveying forage, and the precision and speed of its motion control are crucial factors influencing the efficiency of forage harvesting. In this context, we proposed a trajectory tracking control method for the discharge arm based on an improved particle swarm optimization (IPSO)-PID controller. Firstly, we utilized the Denavit-Hartenberg (D-H) model of the discharge arm for a positive kinematics solution and the geometric resolution and linear fitting method for the inverse kinematics solution. Secondly, we employed the polynomial interpolation method for trajectory planning on the joint space of the discharge arm, and the PSO algorithm for time-optimal trajectory planning. Then, we designed an IPSO-PID trajectory tracking control algorithm and built an Amesim-Simulink co-simulation model for multiple simulation experiments. Finally, we conducted several performance tests of the discharge arm automatic control system in the workstation and the field, respectively. The experiment results indicate that the performance of the IPSO-PID controller exceeds that of all other controllers, which can meet the discharge arm’s motion control accuracy and speed requirements. Our research results are of great significance for improving the productivity and automation process of the self-propelled forage harvester and provide valuable references for research on automatic and precise control of material loading in other agricultural cooperative harvesting.

Portable, heterogeneous ensemble workflows at scale using libEnsemble

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble’s dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble’s generator–simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble’s capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. The study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

Sizing large-scale industrial heat pump for heat recovery from treated municipal sewage in coal-fired district heating system

Electrification of district heating and deep integration of sectors of national economies are fundamental elements of the future smart energy systems. This paper discusses the problem of optimal sizing of large-scale high-temperature heat pumps using treated sewage water as a heat source in a coal-fired district heating system. The study presents an approach to modelling of heat pump system that enables techno-economic analysis for investment decision making. Such analysis is enabled by a black-box-type identification model of the selected industrial heat pump. The model was developed based on the data generated by physical modelling of the heat pump using Ebsilon Professional software. In addition, it is proposed that the heat pump system is integrated with a dedicated photovoltaic power plant. The case study takes into consideration site-specific technical, economic, ecological, and legal constraints, weather conditions, hydraulic performance of the heat-ing network, and variability of loads within the sewage and the district heating systems. The results revealed that the proposed modelling approach is effective regarding multiple simulations and system optimisation. In addition, it was found that large-scale heat pump projects can be technically feasible and profitable if the heat pump is appropriately sized and operated. In the given case, the optimum size of the heat pump for a city of around 180 000 inhabitants is around 12 MW under maximum winter load.

Lane-free signal-free intersection crossing via model predictive control

The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.

Analytical model for CO2-water displacement with rate-dependent phase permeability for geological storage

Analytical modelling is an effective tool for predicting reservoir behaviour under high uncertainties, like CO2 storage in aquifers, where multiple simulation runs are necessary for stochastic modelling and risk assessment. This paper formulates the Buckley-Leverett problem with x-dependent fractional flow. We derive a new analytical model for displacement of brine by CO2 accounting for (i) rate-dependent phase permeability during radial flows and (ii) radial flows of Forchheimer's high-rate gas injection; this analytical model is also valid for (iii) linear flows during flooding in micro heterogeneous or composite cores and (iv) CO2-water flow upscaling in reservoirs where layering is perpendicular to flux direction. An exact solution of the flow equation is based on the observation that the flux of each phase conserves along the characteristic trajectories. We discuss S-shape fractional flow function, which is typical for reservoir rocks. The solution includes formulae for phase saturations and fluxes and trajectories of the displacement CO2-water front and of the forward and rear mixture zone boundaries. The fast analytical model can be used for multivariate sensitivity study and sweep efficiency prediction.

Combining a Multi-Lake Model Ensemble and a Multi-Domain CORDEX Climate Data Ensemble for Assessing Climate Change Impacts on Lake Sevan.

Global warming is shifting the thermal dynamics of lakes, with resulting climatic variability heavily affecting their mixing dynamics. We present a dual ensemble workflow coupling climate models with lake models. We used a large set of simulations across multiple domains, multi-scenario, and multi GCM- RCM combinations from CORDEX data. We forced a set of multiple hydrodynamic lake models by these multiple climate simulations to explore climate change impacts on lakes. We also quantified the contributions from the different models to the overall uncertainty. We employed this workflow to investigate the effects of climate change on Lake Sevan (Armenia). We predicted for the end of the 21st century, under RCP 8.5, a sharp increase in surface temperature and substantial bottom warming , longer stratification periods (+55days) and disappearance of ice cover leading to a shift in mixing regime. Increased insufficient cooling during warmer winters points to the vulnerability of Lake Sevan to climate change. Our workflow leverages the strengths of multiple models at several levels of the model chain to provide a more robust projection and at the same time a better uncertainty estimate that accounts for the contributions of the different model levels to overall uncertainty. Although for specific variables, for example, summer bottom temperature, single lake models may perform better, the full ensemble provides a robust estimate of thermal dynamics that has a high transferability so that our workflow can be a blueprint for climate impact studies in other systems.

Simulation-based inference for parameter estimation of complex watershed simulators

Abstract. High-resolution, spatially distributed process-based (PB) simulators are widely employed in the study of complex catchment processes and their responses to a changing climate. However, calibrating these PB simulators using observed data remains a significant challenge due to several persistent issues, including the following: (1) intractability stemming from the computational demands and complex responses of simulators, which renders infeasible calculation of the conditional probability of parameters and data, and (2) uncertainty stemming from the choice of simplified representations of complex natural hydrologic processes. Here, we demonstrate how simulation-based inference (SBI) can help address both of these challenges with respect to parameter estimation. SBI uses a learned mapping between the parameter space and observed data to estimate parameters for the generation of calibrated simulations. To demonstrate the potential of SBI in hydrologic modeling, we conduct a set of synthetic experiments to infer two common physical parameters – Manning's coefficient and hydraulic conductivity – using a representation of a snowmelt-dominated catchment in Colorado, USA. We introduce novel deep-learning (DL) components to the SBI approach, including an “emulator” as a surrogate for the PB simulator to rapidly explore parameter responses. We also employ a density-based neural network to represent the joint probability of parameters and data without strong assumptions about its functional form. While addressing intractability, we also show that, if the simulator does not represent the system under study well enough, SBI can yield unreliable parameter estimates. Approaches to adopting the SBI framework for cases in which multiple simulator(s) may be adequate are introduced using a performance-weighting approach. The synthetic experiments presented here test the performance of SBI, using the relationship between the surrogate and PB simulators as a proxy for the real case.

Advanced Practice Registered Nurses Student Cross-Specialty Procedural Training: Effective Collaboration and Student Experience.

Intraprofessional simulation and training in acute care nursing specialties can generate synergies that will promote safe, quality patient care. Implementation of multiple intraprofessional simulations across the life span allowed for sharing of faculty and simulation resources. Simulations encompassed both adult and pediatric patients and consisted of airway skills, point-of-care ultrasound, and a multi-skills day encompassing vascular access experiences, chest tube placement, and lumbar puncture. During 5 years, 235 graduate students across three advanced practice nursing specialties participated in the intraprofessional simulation. Learner feedback showed improved confidence, benefit to future practice, and improved clinical judgment via these intraprofessional simulations. Future development of standardized and validated assessments to evaluate each skill will provide quantitative metrics for each clinical skill set and patient population. Further, additional initiatives will include both continuing and expanding intraprofessional simulation offerings, as well as developing interprofessional simulations with physician assistant and perfusionist colleagues. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Distributed Improved RILOS Guidance-Based Formation Control of Underactuated ASVs for Cooperative Maritime Search

A distributed improved robust integral line-of-sight (RILOS) guidance-based sliding mode controller is designed for multiple underactuated autonomous surface vessels (ASVs) to perform cooperative maritime search operations. First, a parallel circle search pattern is designed based on the detection range of ASVs, which can provide the reference formation shape. Second, an improved RILOS method is presented by introducing an integral term into the improved robust LOS method, which can counteract the disadvantageous effect of the unknown sideslip angle and kinematic discrepancy simultaneously. Third, distributed improved RILOS guidance is presented by integrating the extended second-order consensus algorithm into the improved RILOS method; then, the desired heading angle and desired velocity are generated for the control system simultaneously. Finally, the fuzzy logic system is integrated into the sliding mode control (SMC) method to approximate the unknown nonlinear function; then, a distributed improved RILOS guidance-based SMC controller is presented for multiple ASVs. The close-loop signals are proved to be stable by the Lyapunov theory. The effectiveness of the presented method is verified by multiple simulations.