Hasil untuk "River protective works. Regulation. Flood control"

Andreas Burzel, Martine van denBoomen, Matthijs Kok

ABSTRACT Worldwide, cities rely on the proper functioning of critical infrastructures (CIs) such as electricity, telecommunication, water supply and transportation. Failure of those infrastructures can lead to significant and long‐lasting impacts, even far beyond the flooded areas due to cascading effects. Local authorities are eager to take action to reduce flood risk and strive to increase the resilience of their communities. However, CI are often not considered in flood risk assessments. One of the reasons is that CI operators do not share their CI data and internal risk assessments. Therefore, an integral view on flood risk is lacking and risks may be unidentified or underestimated. To overcome this limitation, in this paper we propose an integrated framework for flood risk assessment of urban critical infrastructures (UCIs) for local authorities, which is based on publicly available and field‐surveyed CI data. The proposed framework supports cities to carry out cross‐sectoral risk screenings on urban district level to evaluate the need for in‐depth risk assessments and risk dialogues with CI operators.

Felix Osei, Lianghai Jin

Sam Watkins, Alexandra Collins

Abstract Urban flood risk governance (FRG) approaches increasingly seek to engage local communities—and their surrounding ecosystem in natural flood management (NFM) approaches—to co‐produce socio‐ecological resilience. This systematic review investigates current approaches, barriers, and enablers of community engagement in urban FRG through a flood risk justice lens. Employing a systematic search and an adapted ‘best fit’ framework synthesis methodology, and reporting results according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses transparent reporting system. The central theme of inclusivity emerged from the synthesis, which integrated sub‐themes of relationality, non‐universalism, power structures, and personal paradigms in a conceptual model. Results invite FRG practitioners to reframe community engagement as community inclusion in order to respond to the procedural, social, and environmental justice concerns of urban ‘flood disadvantage’ which may be reinforced by current engagement approaches. Critical discussion of evidence—informed by the conceptual model—recognised five principles for realising procedurally just community inclusion; promoting the co‐production of integrated community inclusion strategies alongside the communities themselves. The study identified a gap in the literature concerning community involvement in NFM; highlighting a priority for future research with a view to realise more inclusive FRG.

Yunus Oruc, Kutay Yılmaz, Serhat Kucukali

ABSTRACT In this study, flood mitigation measures, both structural and nature‐based ones, are proposed, and their social and economic performances are quantified using a multi‐criteria decision analysis framework. The effectiveness of the selected measures is evaluated through numerical modeling. As a case study, flood inundation areas and flood hazard maps are determined in the Ağva District of Istanbul using high‐resolution LIDAR data in a 2D hydrodynamic model for different flood scenarios. The numerical model is calibrated against measured discharges at the river gauging station in the river basin. For the social and economic assessment, a total of seven criteria are assessed: number of affected inhabitants, number of affected cultural items, number of public institutions under flood, expected annual damage, investment cost of measures, annual maintenance cost, and benefit–cost ratio. The overall score of each flood mitigation measure alternative is computed, and their performance is compared for different strategies. For our case study, the implementation of a levee exhibits the highest economic and social performance for flood risk management.

Faith Mitheu, Elena Tarnavsky, Andrea Ficchì et al.

Abstract Skilful flood forecasts have the potential to inform preparedness actions across scales, from smallholder farmers through to humanitarian actors, but require verification first to ensure such early warning information is robust. However, verification efforts in data‐scarce regions are limited to only a few sparse locations at pre‐existing river gauges. Hence, alternative data sources are urgently needed to enhance flood forecast verification to better guide preparedness actions. In this study, we assess the usefulness of less conventional data such as flood impact data for verifying flood forecasts compared with river‐gauge observations in Uganda and Kenya. The flood impact data contains semi‐quantitative and qualitative information on the location and number of reported flood events derived from five different data repositories (Dartmouth Flood Observatory, DesInventar, Emergency Events Database, GHB, and local) over the 2007–2018 period. In addition, river‐gauge observations from stations located within the affected districts and counties are used as a reference for verification of flood forecasts from the Global Flood Awareness System. Our results reveal both the potential and the challenges of using impact data to improve flood forecast verification in data‐scarce regions. From these, we provide a set of recommendations for using impact data to support anticipatory action planning.

Leo Peskett, Sarah Collins, Andrew Black et al.

ABSTRACT There is increasing interest in installing water storage ponds as part of natural flood management (NFM) approaches being implemented globally. Despite decades of experience with constructing flood storage ponds within civil engineering disciplines, there remains little empirical evidence of their effectiveness in NFM. In NFM, ‘natural’ ponds use green infrastructure, are often smaller but more numerous, and are built and maintained by land managers rather than engineers. Here we investigate six flood storage ponds in the 69 km2 Eddleston NFM pilot catchment in Scotland, UK, analysing impact on peak stream flows at different scales and pond designs. The ponds generally reduce peak stream flows where they have large available capacity, catchments are small (< 1 km2), and events are low magnitude (> 20% Annual Exceedance Probability (AEP)). No discernible flow reduction was observed at the largest pond and catchment (64 km2) for the largest (~21% AEP) event. There was significant variability between ponds, and gains can be made in engineering pond inlet/outlet structures, maintenance, and more widespread installation. The findings suggest that natural storage ponds have most potential to contribute to flood control in small catchments (< 10 km2) and small flood events (> 25% AEP), when they are carefully designed and maintained, and sufficient in number.

Jingru Li, Guiying Pan, Yangyu Chen et al.

ABSTRACT Rapid urban flood mapping is crucial for timely risk alerts and emergency relief. Machine learning (ML)‐based mapping models emerge as a promising approach for fast, accurate inundation forecasts. However, current ML models often use precipitation features as inputs and predict maximum flood depth for all grid cells of a specific region simultaneously. This special design improves their prediction efficiency but limits their application in new regions. This study aims to create a highly adaptable, rapid urban maximum flood water depth mapping model based on the random forest regression algorithm and the extreme gradient boosting algorithm. Our mapping model additionally incorporates terrain and land‐use features, besides the precipitation feature, as input variables and generates the maximum water depth only for a grid cell in each mapping. Thus, it can be unchangeably applied to the grid cells in a new area when the model is fully trained. In the case study of Shenzhen, China, our ML‐based mapping model demonstrated excellent mapping ability in both training and validation sets. The coefficient of determination (R2) is consistently greater than or close to 95%. Furthermore, it revealed good generalization ability when directly applied to a new rainfall event (R2 = 0.875) and a new area (R2 = 0.810). Meanwhile, the time cost of the mapping model is less than 3 s, meeting the requirement for real‐time mapping. These results indicate that this highly adaptable model, once appropriately trained, can be applied to rapid urban flood severity mapping, which significantly reduces its use cost in urban flood management.

Abhinav Sharma, Celso Castro-Bolinaga, Natalie Nelson et al.

Fluvial sediment pulses pose a significant threat to the overall ecological health of river systems. Nonetheless, the scarcity of monitored and published data underscores the importance of devising innovative methods for understanding and measuring how river systems react to the introduction of sediments across the fluvial domain. The objective of this study was to create a modeling framework based on reflectance–turbidity that can be applied in regions with both limited and abundant data. Various combinations of predictor variables, training algorithms including linear regression and additional machine learning methods, and input data availability scenarios were examined to comprehend the factors influencing turbidity prediction on a regional scale. The results indicated that, for Washington state, the random forest algorithm, utilizing a combination of reflectance-based predictors and sediment delivery index (SDI) as predictors, produced the most accurate outcomes (data rich: NSE = 0.54, RSR = 0.68, data scarce: NSE = 0.47, RSR = 0.73). However, when tested on three locations in Washington experiencing sediment pulses, the reflectance–based turbidity prediction model consistently underestimated the peak high and peak low turbidity levels for the Elwha River. The model also exhibited consistent inaccuracies in predicting the initial phase of sediment pulses following the Oso Landslide. Nevertheless, promising results were observed for the Toutle River, downstream to the St. Mt. Helens Volcanic eruption site. Overall, the inclusion of SDI in the model enhanced its efficiency and transferability. By enabling the reconstruction of fluvial sediment pulses in data-scarce regions following dam removals, this integrated approach contributes to advancing our understanding of how rivers respond quantitatively and predictively to these disturbances in sediment supply.

Ganesh R. Ghimire, Yan Liu, Esther Parish et al.

ABSTRACT Adapting to future climate change in flood‐prone landscapes will require climate‐resilient agricultural systems. Planting perennial crops, like switchgrass and willow, along river corridors can mitigate future flooding while supporting bioenergy markets. We developed an integrated assessment linking climate, hydrologic, and inundation model results to assess future flood risk to river‐adjacent agricultural lands in the Mid‐Atlantic Region (MAR) and explore this opportunity. We produced ensemble streamflow projections for every MAR stream using a hydrologic model driven by a suite of downscaled and bias‐corrected Coupled Model Intercomparison Project Phase 6 climate projections. We then conducted high‐resolution inundation mapping based on projected flood frequencies for baseline and future periods. Results show that in the near‐term future, at least two‐thirds of the streams will experience 100‐year floods more severe than the baseline 200‐year floods. Riparian zones are projected to face a median rise of inundation by 9.5%–24.1%. Results show that there is an opportunity to mitigate flooding in over half of MAR's counties with the quantities of switchgrass and willow plantings anticipated for mature bioenergy markets, even under the most extreme (200‐year) flood events. Our integrated modeling framework can guide similar regions to evaluate opportunities for flood‐resilient agricultural systems under climate change.

Quentin Bonassies, Thanh Huy Nguyen, Ludovic Cassan et al.

Floods are one of the most common and devastating natural disasters worldwide. The contribution of remote sensing is important for reducing the impact of flooding both during the event itself and for improving hydrodynamic models by reducing their associated uncertainties. This article presents the innovative capabilities of the Surface Water and Ocean Topography (SWOT) mission, especially its river node products, to enhance the accuracy of riverine flood reanalysis, performed on a 50-km stretch of the Garonne River. The experiments incorporate various data assimilation strategies, based on the ensemble Kalman filter (EnKF), which allows for sequential updates of model parameters based on available observations. The experimental results show that while SWOT data alone offers some improvements, combining it with in-situ water level measurements provides the most accurate representation of flood dynamics, both at gauge stations and along the river. The study also investigates the impact of different SWOT revisit frequencies on the models performance, revealing that assimilating more frequent SWOT observations leads to more reliable flood reanalyses. In the real event, it was demonstrated that the assimilation of SWOT and in-situ data accurately reproduces the water level dynamics, offering promising prospects for future flood monitoring systems. Overall, this study emphasizes the complementary strengths of Earth Observation data in improving the representation of the flood dynamics in the riverbed and the floodplains.

Kedi Xie, Martin Guay, Shimin Wang et al.

This paper studies the linear quadratic regulation (LQR) problem of unknown discrete-time systems via dynamic output feedback learning control. In contrast to the state feedback, the optimality of the dynamic output feedback control for solving the LQR problem requires an implicit condition on the convergence of the state observer. Moreover, due to unknown system matrices and the existence of observer error, it is difficult to analyze the convergence and stability of most existing output feedback learning-based control methods. To tackle these issues, we propose a generalized dynamic output feedback learning control approach with guaranteed convergence, stability, and optimality performance for solving the LQR problem of unknown discrete-time linear systems. In particular, a dynamic output feedback controller is designed to be equivalent to a state feedback controller. This equivalence relationship is an inherent property without requiring convergence of the estimated state by the state observer, which plays a key role in establishing the off-policy learning control approaches. By value iteration and policy iteration schemes, the adaptive dynamic programming based learning control approaches are developed to estimate the optimal feedback control gain. In addition, a model-free stability criterion is provided by finding a nonsingular parameterization matrix, which contributes to establishing a switched iteration scheme. Furthermore, the convergence, stability, and optimality analyses of the proposed output feedback learning control approaches are given. Finally, the theoretical results are validated by two numerical examples.

Pavel Osinenko

This work presents a framework for control theory based on constructive analysis to account for discrepancy between mathematical results and their implementation in a computer, also referred to as computational uncertainty. In control engineering, the latter is usually either neglected or considered submerged into some other type of uncertainty, such as system noise, and addressed within robust control. However, even robust control methods may be compromised when the mathematical objects involved in the respective algorithms fail to exist in exact form and subsequently fail to satisfy the required properties. For instance, in general stabilization using a control Lyapunov function, computational uncertainty may distort stability certificates or even destabilize the system despite robustness of the stabilization routine with regards to system, actuator and measurement noise. In fact, battling numerical problems in practical implementation of controllers is common among control engineers. Such observations indicate that computational uncertainty should indeed be addressed explicitly in controller synthesis and system analysis. The major contribution here is a fairly general framework for proof techniques in analysis and synthesis of control systems based on constructive analysis which explicitly states that every computation be doable only up to a finite precision thus accounting for computational uncertainty. A series of previous works is overviewed, including constructive system stability and stabilization, approximate optimal controls, eigenvalue problems, Caratheodory trajectories, measurable selectors. Additionally, a new constructive version of the Danskin's theorem, which is crucial in adversarial defense, is presented.

Xiangyi Chen, Wenbo Huang, Jiaqi Leng

This study develops a water-level management model for the Great Lakes using a predictive control framework. Requirement 1: Historical data (pre-2019) revealed consistent monthly water-level patterns. A simulated annealing algorithm optimized flow control via the Moses-Saunders Dam and Compensating Works to align levels with multi-year benchmarks. Requirement 2: A Water Level Predictive Control Model (WLPCM) integrated delayed differential equations (DDEs) and model predictive control (MPC) to account for inflow/outflow dynamics and upstream time lags. Natural variables (e.g., precipitation) were modeled via linear regression, while dam flow rates were optimized over 6-month horizons with feedback adjustments for robustness. Requirement 3: Testing WLPCM on 2017 data successfully mitigated Ottawa River flooding, outperforming historical records. Sensitivity analysis via the Sobol method confirmed model resilience to parameter variations. Requirement 4: Ice-clogging was identified as the most impactful natural variable (via RMSE-based sensitivity tests), followed by snowpack and precipitation. Requirement 5: Stakeholder demands (e.g., flood prevention, ecological balance) were incorporated into a fitness function. Compared to Plan 2014, WLPCM reduced catastrophic high levels in Lake Ontario and excessive St. Lawrence River flows by prioritizing long-term optimization. Key innovations include DDE-based predictive regulation, real-time feedback loops, and adaptive control under extreme conditions. The framework balances hydrological dynamics, stakeholder needs, and uncertainty management, offering a scalable solution for large freshwater systems.

Han Wang, Di Wu, Lin Cheng et al.

Infinite-time nonlinear optimal regulation control is widely utilized in aerospace engineering as a systematic method for synthesizing stable controllers. However, conventional methods often rely on linearization hypothesis, while recent learning-based approaches rarely consider stability guarantees. This paper proposes a learning-based framework to learn a stable optimal controller for nonlinear optimal regulation problems. First, leveraging the equivalence between Pontryagin Maximum Principle (PMP) and Hamilton-Jacobi-Bellman (HJB) equation, we improve the backward generation of optimal examples (BGOE) method for infinite-time optimal regulation problems. A state-transition-matrix-guided data generation method is then proposed to efficiently generate a complete dataset that covers the desired state space. Finally, we incorporate the Lyapunov stability condition into the learning framework, ensuring the stability of the learned optimal policy by jointly learning the optimal value function and control policy. Simulations on three nonlinear optimal regulation problems show that the learned optimal policy achieves near-optimal regulation control and the code is provided at https://github.com/wong-han/PaperNORC

Imon Chowdhooree, Ishrat Islam

Abstract Non‐governmental organizations (NGOs), governmental organizations, or other entities may run their projects as external interventions within a community to reduce disaster risks and adapt to future climatic events. These external interventions may influence the community to take further adaptation measures for enhancing community resilience. The spontaneous adaptation process, referring as responsive adaptation, needs to be identified and acknowledged. This research aims to investigate the impacts of external interventions on the responsive adaptation process by studying a riverside flood‐prone urban slum in Bangladesh. This settlement experienced a site development project, primarily run by an NGO, that allowed several modifications to the built environment, mainly targeting flood risks. Selected tools of participatory rural appraisal (PRA) or participatory urban appraisal (PUA) methods were employed to obtain data about the community's initiatives for further development. The results show that the site development project, especially its impacts on reducing flood risks, has influenced community members to invest in improving the condition of their individual houses. As a means of responsive adaptation, the conscious developments of their houses contribute to enhancing the resilience level. Through exploring the community's initiatives, this research identifies that the engagement of communities with their knowledge and investments can extend the success of the external intervention.

رضا نوروز ولاشدی, صدیقه برارخانپور احمدی, حدیقه بهرامی پیچاقچی et al.

مقدمه اقلیم، یکی از عوامل محیطی است که تغییر آن موجب تغییرات گسترده‏ای در بخش‌‏های مختلف بوم ‌سامانه شده و تهدید بزرگی برای توسعه پایدار محسوب می‏‌شود. دما، یکی از عناصر اصلی اقلیم است که تغییرات ناگهانی، کوتاه‏مدت و درازمدت آن می‌‏تواند ساختار آب و هوای هر منطقه را تحت تأثیر قرار دهد. در دهه‌‏های اخیر، کره‏ زمین با پدیده‏ گرمایش جهانی مواجه شده است و مهم‌‏ترین شاهد بر این ادعا تغییرات اقلیمی صورت گرفته در سرتاسر دنیا است. یکی از پیامدهای مهم گرمایش جهانی، افزایش وقوع پدیده‏‌های فرین جوی است که از مهم‌ترین آن‏ها می‌‏توان به تغییر ناگهانی دما، گرمای بیش از حد، سرمای غیرعادی، بارش‏‌های سنگین و سیل‏‌آسا، خشکسالی و گرد و غبار ناشی از خشک شدن تالاب‏‌ها اشاره نمود. نمایه‏‌های حدی اقلیم، نه تنها نقش مهمی در بررسی وقایع اقلیمی در مقیاس منطقه‌‏ای و جهانی دارند، بلکه به مدل‌سازی اقلیمی و تصمیم‏‌گیری در بررسی اثرات بخش‌‏های متنوع نیز کمک می‌‏نماید.   مواد و روش‌‏ها در این پژوهش، از داده‎‌های دمای مدل MRI-ESM-2 تحت سه سناریو خوش‏بینانه SSP1-2-6، حد متوسط SSP2-4-5 و بدبینانه SSP5-8-5 برای دو دوره‏ آینده‏ نزدیک (۲۰۶۰-۲۰۲۱) و آینده‏ دور (۲۱۰۰-۲۰۶۱) استفاده شد. بدین منظور، ابتدا داده‏ تاریخی طی دوره پایه و داده‏‌های سناریو برای دوره‏ آینده (تا سال ۲۱۰۰) برای مدل اقلیمی مورد مطالعه از پایگاه داده ESGF برای کل جهان دریافت شد. سپس، با زبان برنامه نویسی R، سری زمانی داده‏‌های تاریخی و سناریو از مدل برای هر ایستگاه مورد نظر استخراج شد. مقیاس‌کاهی آماری داده‌‏ها با روش درون‏یابی دوخطیBilinear  در سطح ایستگاه‌‏های مورد مطالعه انجام شد. در ادامه، داده‌ها در دوره آینده و بر اساس سه سناریوی مورد نظر در تمامی ایستگاه‌‏های مورد مطالعه گروه‌‏بندی شدند، برای استخراج نمایه‌‏های مبتنی بر کمینه و بیشینه دمای روزانه از نرم‌افزار RClimDex  استفاده شد. در این پژوهش، ۱۶ شاخص حدی دما برای منطقه مورد مطالعه در مقیاس‌های سالانه و ماهانه محاسبه و وجود روند و نقطه شکست در این شاخص‏‌ها با آزمون‌های آماری تشخیص روند من‌کندال، آزمون شیب خط سن و آزمون تشخیص جهش پتیت بررسی شد.   نتایج و بحث نتایج بیانگر کاهش رویدادهای حدی گرم بر اساس سناریو SSP126، کاهش نمایه‌‌های مربوط به سرما و روزهای یخبندان (سرد) و افزایش نمایه‎‌های حدی گرم براساس سناریو SSP585 بوده که در بیشتر مناطق استان مشاهده شد. به طورکلی، نمایه‎‌های تعداد روزهای تابستانی (با شیب ۷۰-۴۰ درصد)، شب‎ه‌ای حاره‎ای (۶۵-۴۵ درصد)، طول دوره گرما (۵۰-۳۰ درصد) و طول دوره رویش (۶۰-۴۰ درصد) به طور قابل ملاحظه افزایش اما نمایه‎‌های تعداد روزهای یخبندان ((۲۰-)-(۸۰-))، روزهای یخی ((۱۰-)-(۴۰-)) و طول دوره سرما ((۱۰-)-(۷۰-)) به‌طور قابل ملاحظه کاهش خواهد یافت. همچنین، نتایج آزمون پتیت، نقطه تغییر افزایشی و کاهشی به‌ترتیب برای نمایه‎‌های حدی گرم و سرد را در دهه ۲۰۴۰ (آینده نزدیک) و ۲۰۸۰ (آینده دور) نشان داده است. بنابراین، به‌منظور کنترل دماهای حدی و اثرات سوء آن در بخش‏‌های مختلف زندگی انسان به‌ویژه کشاورزی و منابع آب می‏بایست برنامه‏‌های مدیریتی مناسب در جهت نیاز هر منطقه تدوین و اجرا شود.   نتیجه‏‌گیری نتایج بیانگر وجود تغییرات ناگهانی برای نمایه‌‏های دمایی تحت سناریو بدبینانه بیشتر از دو سناریو دیگر و در نواحی گسترده‌‏تری از استان مازندران بود. همچنین، احتمال افزایش ناگهانی نمایه‌‏های حدی گرم و کاهش ناگهانی روزهای مربوط به سرما و یخبندان در دوره‏ زمانی آینده نزدیک در دهه ۲۰۳۰، ۲۰۴۰ و ۲۰۵۰ و در دوره‏ زمانی آینده دور در دهه ۲۰۷۰ و ۲۰۸۰ وجود خواهد داشت. به طورکلی، در دوره‏‌های آماری آینده مورد مطالعه، نمایه‌‏های حدی دمایی تغییرات قابل توجهی خواهند داشت و استان مازندران با افزایش دمای هوا و رخدادهای حدی درجه حرارت بالا همراه خواهد بود که این نتایج همسو با نتایج مطالعات منطقه‌‏ای و جهانی است. افزایش دما به‌ویژه در ماه‏‌های گرم که همزمان با کاهش نزولات جوی است، با توجه به ماهیت فصل گرم سال، در کشاورزی این منطقه که از مناطق مهم تولید برنج کشور است، نقش قابل توجهی دارد. لذا، چرخه هیدرولوژی پایین‌دست حوضه هراز را تحت تأثیر قرار می‏‌دهد. از طرفی، تغییرات دمایی در زمستان و ماه‏‌های سرد نیز می‏تواند زمان آغاز ذوب برف حوضه را تحت تأثیر قرار دهد که این عوامل روی دبی اوج سیلاب در پایین‌دست حوضه تأثیر به‌سزائی دارد. با توجه به مطالب ذکر شده و ضرورت انجام چنین پژوهش‌‏هایی در زمینه‏‌های فعالیت‌‏های انسانی، مدیریت منابع آب، امنیت غذایی و نیز سلامتی انسان، بررسی تأثیر رویدادهای حدی اقلیمی مبتنی بر دما در سیاست‎‌گذاری‏‌های آینده در بخش‏‌های مختلف ضرورت دارد و جوامع انسانی می‏‌بایست به ناچار خود را بر اساس این شرایط تنظیم و سازگار نمایند. لذا، بررسی شدت، فراوانی و زمان وقوع رخدادهای حدی و آگاهی احتمالی از آنها می‌‏تواند در حل مسائل گریبان‏گیر زیست‏ محیطی و برنامه‌‏ریزی منطقی در جهت کنترل و کاهش این رخدادها مؤثر واقع شود.

Tao Huang, Venkatesh Merwade

Abstract Evaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics.

Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer's estimation accuracy and the fulfillment of safety and control requirements.

Daniel Zelazo, Shin-ichi Tanigawa, Bernd Schulze

This work considers the distance constrained formation control problem with an additional constraint requiring that the formation exhibits a specified spatial symmetry. We employ recent results from the theory of symmetry-forced rigidity to construct an appropriate potential function that leads to a gradient dynamical system driving the agents to the desired formation. We show that only $(1+1/|Γ|)n$ edges are sufficient to implement the control strategy when there are $n$ agents and the underlying symmetry group is $Γ$. This number is considerably smaller than what is typically required from classic rigidity-theory based strategies ($2n-3$ edges). We also provide an augmented control strategy that ensures the agents can converge to a formation with respect to an arbitrary centroid. Numerous numerical examples are provided to illustrate the main results.