System Constraints Research Articles

An energy management method for combined load forecasting of fuel cell ships

Abstract The article proposes an energy management method (EMM) for fuel cell ships, which consists of two parts: load forecasting (LF) and real-time optimized scheduling (RTOS). LF is composed of Long Short-Term Memory neural networks, which are capable of being trained to predict future loads based on historical ship load information. RTOS consists of model predictive control and can allocate energy in real time. Initially, shiploads and battery status are collected in real time, and then LF passes the predicted load sequence to RTOS. RTOS optimizes real-time energy allocation based on the predicted load and the current operating status of two sets of batteries, while meeting system constraints and maintaining the battery SOC at a healthy level, to minimize operational cost consumption. The simulations demonstrate that the EMM can consistently maintain SOC at a healthy level and reduce fuel cell ship operating costs to some extent, thereby improving economic efficiency.

IEA Wind Task 49: Reference Site Conditions for Floating Wind Arrays

Abstract The commercial-scale deployment of floating offshore wind (FOW) projects is expected to take place in a diverse range of sites that may differ significantly from existing fixed-bottom projects. FOW farms are particularly sensitive to the water depth and the meteorological and oceanographic (metocean) and geotechnical conditions at the project site due to the wave-induced system motions and loads as well as the anchoring system constraints imposed by the seafloor conditions. Uncertainty around the site conditions will permeate through all aspects of project design, leading to suboptimal and overly conservative designs, increased costs, and adversely affected performance. As FOW expands into a global industry, metocean and geotechnical conditions will increasingly vary for projects located in different geographic regions or in far-from-shore, deep-water sites. This study represents the outputs of work package 1 of International Energy Agency Wind Task 49, which focuses on the integrated design of floating wind arrays. The primary goal of this study is to establish the type of parameters and constraints required to characterize FOW array reference sites; provide a realistic and publicly available set of reference site conditions to the FOW community as a baseline set of data for individual research projects; identify and categorize any critical gaps in the existing data or methodologies required to define reference site characteristics; and inform and support the design of reference FOW arrays. A building block concept was developed for synthesizing reference sites for the design of FOW arrays. The building blocks include three classes of site conditions focusing on the techno-economic design of FOW projects: metocean conditions, seabed conditions, and coastal infrastructure. All reference site data produced and collected in this study are publicly available.

Multi-Objective Numerical Analysis of Horizontal Rectilinear Earth–Air Heat Exchangers with Elliptical Cross Section Using Constructal Design and TOPSIS

This study presents a numerical evaluation of a Horizontal Rectilinear Earth–air Heat Exchanger (EAHE), considering the climatic and soil conditions of Viamão, Brazil, a subtropical region. The Constructal Design method, combined with the Exhaustive Search, was utilized to define the system constraints, degree of freedom, and performance indicators. The degree of freedom was characterized by the aspect ratio between the vertical and horizontal lengths of the elliptical cross-section duct (H/L). The performance indicators for the EAHE configurations were assessed based on thermal potential (TP) and pressure drop (PD). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied for multi-objective evaluation, and a methodology for EAHE is proposed. The problem was solved using FLUENT software (version 2024 R2), which employs the Finite Volume Method to solve the conservation equations for mass, momentum, and energy. The (H/L)T,o = 6.0 configuration showed a 16.4% increase in thermal performance for heating and 15.9% for cooling compared to the conventional circular duct. Conversely, the (H/L)F,o = 1.0 configuration reduced pressure loss by 65.33%. The integration of Constructal Design with TOPSIS facilitated the identification of optimized geometries that achieve a balance between performance indicators and those that specifically prioritize thermal or fluid dynamic aspects, being this approach an original scientific contribution of the present work.

Physics-informed neutral network with physically consistent and residual learning for excavator precision operation control

The data-driven methodologies can establish accurate Inverse Dynamics Model (IDM) of the excavator thus improving control precisions. However, the inherent black-box nature of these models often results in overfitting to the dataset, leading to predictions that deviate from the constraints of physical system. Consequently, this can lead to controller failures, introducing unpredictable behavior that threatens operation precision. In addition, the uncertainty of the external disturbance poses great challenge to the precision of controller. This study presents a physics-informed neural network to build accurate IDM with physical consistency. The Rigid Body Dynamics (RBD) of the excavator are coupled within a Deep Lagrangian Network (DeLaN), while a Convolutional Neural Network (CNN) and a Long Short-Term Memory Network (LSTM) are employed to assimilate the residual nonlinear characteristics, such as hydraulic flexibilities and stick–slip friction. To the uncertainty of the external disturbance, the Prescribed Performance Inverse Dynamics Controller combination with the DeLaN-CNN-LSTM model (PPIDC-DCL) is constructed for precise control by constraining the control error within a finite region. The experimental results demonstrate that the model captures the underlying structure of the dynamic and builds the IDM with high accuracy and robustness. Moreover, the PPIDC-DCL controller effectively constrains the control error and realizes precision control. The proposed method has potential applications and provides novel insights for achieving precise operation control of excavators.

Adaptive control scheme for cooperative transport systems navigation under uncertainty

An adaptive robust control scheme for a cooperative transport system is proposed to tackle the challenges arising from parameter uncertainty, external interference, measurement errors, and other factors. The cooperative transport system consists of a leader automated ground vehicle, a baggage carrier, and a follower automated ground vehicle. Firstly, a mechanics-based independent dynamic model without constraints is established for each component within the system, and the coupling relationship between components is analyzed to design the constraint function. Secondly, to address inaccuracies in initial conditions, the trajectory errors in both their zero- and first-order forms are introduced. A closed-form dynamic model that is subject to constraints is then developed for the entire cooperative system, incorporating both structural and performance constraints. Thirdly, an innovative adaptive robust control scheme is introduced for mechanical systems facing uncertainty. An adaptive law is devised to estimate the bounds of uncertainty. The control is deterministic and can be mathematically expressed in a closed-form. Fourthly, a constrained optimization problem is formulated using the fuzzy information of uncertainty to choose an appropriate optimal gain kopt of the adaptive law. This is achieved by minimizing the combination of average fuzzy system performance and control effort. Finally, numerical simulations are conducted to verify the effectiveness and adaptability of the proposed control method. The performance of controlled cooperative transport system is both deterministically guaranteed and fuzzily optimized.

Hermitian solution to constraint system of generalized Sylvester quaternion matrix equations

The different systems of Sylvester quaternion matrix equations have prolific functions in system and control. This paper considers a Hermitian solution of a system of Sylvester quaternion matrix equations over a quaternion algebra H. If some necessary and sufficient conditions are fulfilled, the general solution to these quaternion matrix equations is expressed by explicit representation formulas in terms of generalized inverses. We provide an algorithm and a numerical example based on the original direct method using determinantal representations of the quaternion Moore–Penrose inverse.

Enhancing energy control for a hybrid system comprising a wind turbine, diesel generator, and storage systems

The increase in demand for electrical energy has made renewable energy sources, such as wind power, increasingly attractive. However, intermittency and variability represent the major problems of these types of energies. The hybridization of several energy sources allows to have a reliable and efficient supply system. This paper was interested in the control of a hybrid energy system, which includes a wind turbine, storage systems (batteries and super-capacitors) and a conventional diesel generator, with the aim of generating constant power. A control strategy is developed taking into account the various constraints of the hybrid system. Both of storage systems as well as the diesel generator operate according to the modes presented in the supervision algorithm to absorb and compensate the fluctuation of the wind energy and reach to a constant energy flow. The super-capacitor operates before the battery to absorb the power peaks in both charge and discharge modes. The hybrid system is simulated using MATLAB/Simulink software, and the results are provided in order to show the effectiveness of this control strategy.

A comparative analysis of intelligent energy modelling approaches for a grid-tied PVT system application

Renewable energy resources are a long-term answer to the current chronic energy dilemma. Solar photovoltaic (PV) systems are the most often used form of a practical solution for power supply and energy management in buildings. A PV-thermal (PVT) system is essential to lowering the demand for grid energy by capturing electrical and thermal energy from sunlight, optimising energy usage and encouraging sustainable energy practices. The most significant difficulty faced by developing countries, such as South Africa, is a lack of power supply to meet demand while limiting grid overload. An alternative source of energy is a critical component. This research compares two intelligent energy modelling methodologies: optimal control scheme-based open loop and model predictive control (MPC) in a PVT application. The primary goal of the modelling is to limit the electricity required from the grid while stressing the system’s energy efficiency and cost-effectiveness. The open loop approach uses mathematical optimisation techniques to establish the best control strategy for the PVT system. The ideal set-points for various system parameters are found by presenting the problem as an optimisation task to reduce grid power usage. The optimal control technique delivers a global optimisation solution based on the system dynamics and constraints. The MPC technique employs a predictive control technique of the PVT system to create optimal control actions in a receding horizon manner. The MPC algorithm anticipates future system behaviour and generates control actions that minimise grid power drawn by iteratively solving an optimisation problem at each time step. This robust online control scheme enables real-time modifications and responses to system disruptions that effectively and dynamically coordinate system behaviour.

Obstacle avoidance control of UAV formation based on distributed model prediction

Aiming at the formation and maintenance problem of UAVs in an obstacle environment, a distributed model predictive control (DMPC) algorithm with no reference trajectory considering system constraints is proposed. In order to deal with the constraint coupling and cost coupling existing in model predictive control (MPC), the assumed trajectory is introduced to design a low conservative compatibility constraint and a cost function of no reference trajectory, so that the algorithm can be executed in a distributed and synchronous manner. Secondly, the terminal constraint is designed based on the speed barrier method to ensure the safety of the terminal domain, and a feasible terminal control input is given. The cost function is taken as a Lyapunov function, combined with the constructed stability constraint, and the iterative feasibility and system stability of the algorithm are analyzed. In addition, in order to take into account the real-time performance, a non-strictly stable DMPC algorithm that can better meet the requirements of formation obstacle avoidance is given on the basis of the proposed algorithm. The effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes considering load management

This study focuses on improving power system grid performance and efficiency through the integration of distributed energy resources (DERs). The study proposes an artificial intelligence (AI) based effective approach for economic dispatch and load management for three linked microgrids (MGs) that operate in both grid-connected and autonomous modes. A day-ahead scheduling method is suggested to calculate the optimal set points for various energy sources in MGs considering various system constraints for safe operation. In addition, a load management approach that shifts the controllable loads from one interval to another is applied to reduce the operating cost of MG. To handle the optimization challenges of energy scheduling and load shifting such complexity and non-linearity, an advanced meta-heuristic method known as the one-to-one based optimizer (OOBO) is used. Overall, the paper proposes a viable and efficient methodology for economical distribution in linked microgrids, which takes advantage of renewable energy resources and incorporates scheduling optimization via the OOBO algorithm. The proposed energy management strategy enhances the system performance, increases energy efficiency, and reduces the daily operational cost by 1.6% for grid connected mode and by 0.47% for islanded operation mode.

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

The state of the system generally satisfies specific constraints imposed by material properties or physical laws, so the application of these constraints can improve the accuracy of state estimation. In this paper, a novel recursive filter referred as constrained high-degree cubature skew-Gaussian filter (CHCSGF) is proposed, which achieves soft-constrained state estimation by compressing the probability density of unconstrained states with constraint information. First, the probability density of the state under inequality soft constraints is modeled as a skew-Gaussian (SG) distribution, rather than truncated or single Gaussian distributions. Then, a recursive constrained SG filter is developed to handle inequality soft constraints in linear systems. Addressing nonlinear challenges, a 5th-degree spherical-radial cubature approximation method is presented to numerically calculate SG-weighted integrals for the nonlinear transformation of SG distribution. Finally, the CHCSGF algorithm is proposed using this method to tackle nonlinear filtering problems. The CHCSGF is applied to reentry trajectory tracking to improve estimation accuracy by dealing with heat flow, dynamic pressure and overload constraints during reentry flight. Simulation results demonstrate that the CHCSGF achieves higher estimation accuracy than unconstrained methods under nonlinear inequality soft constraints, and is robust to the constraints with a prior error. Compared to particle filter and moving horizon estimation, the computational complexity of CHCSGF is significantly reduced.

Implications of Constraints on the Language of Emerging Aided Communicators and Intervention Decisions: A Tutorial

This tutorial offers speech-language pathologists (SLPs) an opportunity to consider the theoretical constructs of language acquisition theory and the multifactorial determinants that can impinge on the language acquisition process for children with complex communication needs. The system of constraints observed through the lens of optimality theory is discussed alongside intrinsic and extrinsic barriers associated with children who acquire their first language simultaneous to their introduction to aided augmentative and alternative communication (AAC) systems. This examination calls into question whether the language competence of emerging aided communicators may be masked by their ability to produce language that accurately represents their understanding of their native language. Specific to this question is whether vocabularies available to emerging aided communicators, opportunities to access aided language models, and intervention decisions made by SLPs may be implicated in the language acquisition and development process. Suggestions for clinical practice are offered as a means of mitigating adverse outcomes related to extrinsic constraints.

Nonlinear Model Predictive Control of Heaving Wave Energy Converter with Nonlinear Froude–Krylov Forces

Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude–Krylov forces, which are essential in accurately representing the system’s behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC’s safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude–Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinear model (NLMPC) incorporating exact Froude–Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinear model also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC.

Retraction Note: Solar energy harvesting to optimise the power constraints in 5G systems

Retraction Note: Solar energy harvesting to optimise the power constraints in 5G systems

Regaining focus: Promoting attentional literacy in digital higher education

The attentional demands placed on digital learners have grown, with online and blended education increasingly impacted by hyperconnectivity and digital disarray. In this context, it is essential for educators to help learners develop attentional literacy (AL). Building on past research on AL which fused insights from the fields of digital literacies and contemplative pedagogy (CP), this Delphi study examined the concept of AL and associated practices in higher education. Starting with a working definition of AL, expert feedback was invited from a global panel of experienced CP practitioners across academic disciplines. Through three Delphi rounds, the AL definition was validated and refined, before an abbreviated version was produced to facilitate operationalisation by digital educators who may not have a CP background. The study further explored how AL can be integrated into online higher education curricula, identifying strategies for educators and students to develop AL practices and address barriers to these practices. Despite systemic and structural constraints, cultivating AL allows educators and students to exercise a greater degree of individual and collective agency over their own attention in a digital world. Implications for practice or policy: Students can be guided in developing AL through a series of stages involving awareness and noticing, focus and intentional choice, openness and curiosity, and consideration of the wider attentional ecosystem. Educators should develop their own AL first, approaching it holistically by integrating personal, pedagogical and professional development-related practices, along with complementary offline activities. Institutions can maximise scope for AL development by increasing technological support and, especially, reducing curricular time pressure on educators and students.

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Primal characterizations of stability of error bounds for semi-infinite convex constraint systems in Banach spaces

This article focuses on the stability of error bounds, both local and global, for semi-infinite convex constraint systems in Banach spaces. We present primal characterizations of the stability of these error bounds under small perturbations. These characterizations are expressed through the directional derivatives of the functions that define the systems. It is shown that ensuring stability of the error bounds is closely tied to verifying that the optimal values of several minimax problems, formulated using the directional derivatives, remain outside a certain neighbourhood of zero. Furthermore, this stability condition only requires that all component functions of the system share the same linear perturbation. When applied to the sensitivity analysis of Hoffman's constants for semi-infinite linear systems, these stability results yield primal criteria that guarantee the uniform boundedness of Hoffman's constants under perturbations in the problem data.

Predefined-time adaptive fuzzy control for nonlinear output constraint systems with unknown backlash-like hysteresis via command filtering

In this article, the predefined-time adaptive fuzzy control problem for strict-feedback nonlinear systems with asymmetric time-varying output constraint and unknown backlash-like input hysteresis is considered. An innovative predefined-time adaptive control method, in which utilises unified barrier function (UBF) to deal with the output constraint problem and adopts a differential equation to represent the unknown backlash-like hysteresis, is presented by combining a novel command filter with backstepping technology. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinearities. Moreover, by designing appropriate parameters, the constructed control strategy ensures that all signals are predefined-time bound and the tracking error stays in a small neighbourhood adjacent zero. Ultimately, two simulation examples are given to prove the effectiveness of the presented control algorithm.

Implementation of Respect for Autonomy in Hospital Services Within the Indonesia National Health Insurance System

The principle of patient autonomy forms the foundation of medical ethics. However, its exploration within the context of national health insurance systems in developing countries remains under-researched. This study aimed to evaluate respect for patient autonomy within Indonesia's National Health Insurance (NHI/JKN) system. The study using a qualitative research interview analyzed thematically. Conducted in Depok, West Java, the study involved in-depth semi-structured interviews with 18 participants, encompassing patients from first-level health facilities (FLHF), general practitioners at FLHF, specialist doctors, and management of referral hospital (RH) officers, and staff members of the JKN. The data were transcribed and analyzed using a thematic approach. The study revealed substantial underutilization of respecting patient autonomy within medical contexts under the JKN. Five themes emerged: challenges in the referral system, knowledge and information dissemination, decision-making and autonomy, quality of healthcare services, and systemic constraints and impact. These themes highlight the lack of patient awareness, restricted medical choices, the dominant role of paternalism (a system in which the government or a person in a position of authority makes decisions for other people) in healthcare decisions, and improper informed consent process. The findings suggest that the principles of beneficence and paternalism often overshadow respect for patient autonomy in the JKN system. This raises concerns about the ethical aspect of patient treatment, particularly the need for greater focus on patient autonomy and shared decision-making to align more closely with global medical ethics practices. This study contributes to understanding autonomy in national health insurance systems in developing countries. It highlights the need for systemic reforms to enhance healthcare efficiency and effectiveness while respecting patient autonomy.

Microservices Deployment Strategies: Navigating Challenges with Kubernetes and Serverless Architectures

This paper aims at exploring the effects of performance tuning on concerns such as reliability and uptime with respect to the three current architectures: cloud, on-premise, and hybrid models. This paper shows that performance tuning does enhance system stability and availability based on comparative data on CPU load, memory consumption, disk I/O operations, network delays, application response time, and system failure rates. Employing both quantitative and qualitative data, the study compares features taken from real-world system logs and information obtained from expert interviews indicating that tuning brings about balanced resource distribution as well as ensures faster data processing free from system constraints. The results presented prove that performance tuning positively affects the availability of the system and lowers the failure rates significantly after tuning. This work underscores the need for pre-emptive approach to performance calibration as a means of guaranteeing the systems availabilities and reliability where systems integration is complex and distributed across several nodes. The conclusions help those organizations striving for improvements of the systems in their concern areas for better performances and less failures in a world where competition and operations demands are growing.