Control Rules Research Articles

Increasing Precursor Reactivity Enables Continuous Tunability of Copper Nanocrystals from Single-Crystalline to Twinned and Stacking Fault-Lined.

Colloidal nanocrystals (NCs) are active materials in different applications, wherein their shape dictates their properties, such as optical or catalytic properties, and, thus, their performance. Hence, learning to tune the NC shape is an important goal in chemistry, with implications in other fields of research. A knowledge gap exists in the chemistry of non-noble metals, wherein design rules for shape control of NCs are still poorly defined compared to those of other classes of materials. Herein, we demonstrate that tuning the precursor reactivity is crucial to obtaining a continuous shape modulation from single-crystalline to twinned and stacking fault-lined Cu NCs. This tunability is unprecedented for non-noble metal NCs. We achieve this result by using diphenylphosphine in place of the most commonly used trioctylphosphine. Using in situ X-ray absorption spectroscopy, we show that the temperature modifies the reaction kinetics of an in situ-forming copper(I)bromide-diphenylphosphine complex during the synthesis of Cu NCs. We propose the presence of a P-H functionality in the phosphine to explain the higher reactivity of this precursor complex formed with diphenylphosphine compared to that formed with trioctylphosphine. This work inspires future studies on the role of phosphine ligands during the synthesis of Cu NCs to rationally target new morphologies, such as high-index faceted Cu NCs, and can be conceptually translated to other transition-metal NCs.

Advanced Design of Naval Ship Propulsion Systems Utilizing Battery-Diesel Generator Hybrid Electric Propulsion Systems

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles.

Autonomous Cooperative Hunting with Rule-Based and Self-Learning Control for Multiagent Systems

This paper considers the problem of autonomous cooperative hunting in an unknown dynamic environment, where a group of mobile agents collaborate to capture a moving target. Due to the decentralized decision-making nature of multi-agent systems and the presence of real-world constraints, it is a challenging task. To solve this problem, an artificial rule based hunting algorithm (AR-HA) is firstly developed based on the principles of attraction and repulsion with heading adjustment, and each agent is controlled by the designed rules. Then, to further enhance the stability of cooperative hunting, a self-learning algorithm based on Twin Delayed Deep Deterministic policy gradient (SL-TD3) is proposed. Each agent is governed by its own SL-TD3 controller and learns independently from its interaction with the environment, taking advantage of the reward function designed based on the control rules of AR-HA. Besides, in order to improve training efficiency, imitation learning is employed to initialize the actor network. Experiments on both virtual and real robots demonstrate the effectiveness of the proposed algorithms for autonomous cooperative hunting.

Research on motion control strategy of athlete muscle training based on blockchain and visual image analysis

Muscle training is an important part of athletes’ physical training. Its goal is to keep athletes in high intensity and improve sports performance in physical training. With the continuous improvement of the level of competitive sports, there are still many problems in the training of competitive athletes. The research on the control strategies of some technical movements in the process of athletes’ muscle development would help to improve the efficiency and effect of athletes’ training. By analyzing and quant description of muscle movement control process based on blockchain and visual image processing technology, the muscle movement control rule is discussed and then a complete movement database is constructed. At the same time, combined with the sports experiment method and cognitive behavioral theory, this paper analyzed the exercise load, exercise time allocation, muscle training time allocation methods and the corresponding muscle training strategy selection methods under different muscle states, providing theoretical basis for athletes to conduct muscle strength training scientifically and efficiently. This paper first analyzed the importance of athletes’ muscle training action control. After that, the research was mainly carried out from the following two aspects. The first is to analyze the motion control strategy of athlete muscle training based on blockchain and visual image, and summarize its characteristics. The second is to use electronic imaging technology to analyze the data related to posture control in athletes’ muscle training, and explore the characteristics and changes of muscle posture. At last, this paper put forward relevant algorithms about visual images and conducted experimental research on athletes’ physical indicators and performance under different muscle training according to the research in this paper. It was concluded that the score ratio of athletes’ performance after muscle training action control was 7.39% higher than that of general muscle training. Therefore, it is very important to strengthen the research of movement technology in muscle training and analyze the influence of some training control strategies on the development of athletes’ muscles in training.

Automatic control strategy of electronic and electrical engineering based on FPGA

Field Programmable Gate Array (FPGA) have demonstrated significant potential in the field of automation control due to their flexibility, programmability, and high performance. This study designs an FPGA-based automation control strategy for motor control systems, encompassing four core modules: signal acquisition, signal processing, control algorithm, and output drive. The signal acquisition module employs the AD9280 high-speed, high-precision ADC chip to achieve rapid and accurate signal acquisition. The signal processing module utilizes digital filtering and FFT analysis within the FPGA to extract key information such as motor speed. The control algorithm module adopts an adaptive fuzzy control strategy that fine-tunes control rules in real-time to ensure precise control. The output drive module generates PWM signals via the FPGA to drive the motor, guaranteeing efficient and stable operation. Experimental results indicate that the FPGA-based control strategy significantly outperforms traditional methods in terms of response speed and stability, capable of meeting the high-performance requirements of modern industrial motor control systems. This research provides new technical insights and practical references for the field of automation control in electrical and electronic engineering.

Relay Switching–Based Event‐Triggered Finite‐Time Tracking Control for Nonholonomic Systems: Theory and Experiment

ABSTRACTThis paper investigates the event‐triggered finite‐time trajectory tracking controller based on a type of chained‐form nonholonomic systems under unknown disturbances by utilizing relay switching technology. A key design idea is that the entire control design is grouped into two separated control stages to produce different finite‐time tracking controllers with correspondingly event‐triggered control rules. By using some nonlinear design methods, a relay switching–based event‐triggered finite‐time tracking control method is adopted to assure that the resulting closed‐loop tracking error system converges to an arbitrarily small neighborhood around zero within a finite time. All closed‐loop error system states keep bounded throughout the entire process. Simulation and experiment results demonstrate the rationality of the designed tracking control strategy.

Dorsal fin height is not an effective tool to distinguish hatchery and wild steelhead in the field

AbstractObjectiveTo maximize harvest of hatchery steelhead Oncorhynchus mykiss and Pacific salmon Oncorhynchus spp. while reducing impacts to wild conspecifics, fishery managers rely on the ability of anglers to distinguish between the two. Fin erosion, including dorsal fin erosion, is a common result of hatchery rearing of juvenile salmonids. Since the 1980s, managers in the Pacific Northwest have utilized adult steelhead dorsal fin height as a regulatory control rule, requiring anglers to release steelhead with dorsal fins over a height threshold. This approach assumes that all or most hatchery fish have shorter dorsal fins than their wild counterparts. This study was designed to test this assumption.MethodsWe compared steelhead origin (hatchery or wild) determined using scale analysis and adipose fin clipping (hatchery n = 127, wild n = 71) with origin estimated using dorsal fin height.ResultOverall, a static dorsal fin height threshold (2.25 in, 5.72 cm) correctly identified 90% of hatchery‐origin steelhead but misidentified nearly half (44%) of wild fish as hatchery. Dorsal fin height of known hatchery fish correlated with fish length, indicating a weakness of using a static dorsal height threshold as a management tool. The accuracy of distinguishing wild fish improved, but only marginally (56–76%), using a classification model where dorsal fin height of known origin fish from this study was a continuous predictor of origin. In comparison, adipose fin status correctly identified 97% of unmarked fish as wild.ConclusionThe low accuracy of the classification model and static dorsal fin height thresholds at distinguishing hatchery and wild steelhead due to a strong correlation between dorsal fin height and fork length and the high fork length variability of hatchery origin fish. Given the low cost and accuracy of adipose fin clipping and the potentially high conservation risk associated with misidentification, we recommend discontinuing the use of dorsal fin height as a harvest control rule for steelhead.

Examining the role of national governance capacity in building the global low-carbon agricultural supply chains

The majority of related research has traditionally focused on examining the individual influences of national governance capacity and technological innovation on carbon emissions and economic performance, neglecting the effects and influence mechanisms on carbon efficiency within agricultural supply chains, which is not conducive to advancing a low-carbon transition that includes agricultural concerns. This study addresses these gaps by investigating the correlation between national governance capacity (considering voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law and corruption control) and agricultural supply chain carbon efficiency and investigating the influence mechanism and mediating role of technological innovation. The relevant findings are twofold. 1) The impact of national governance capacity and its components on agricultural carbon efficiency follows an inverted U-shaped curve. 2) Technological innovation can act as a mediator between national governance capacity and select factors of voice and accountability, government effectiveness and regulatory quality to enhance agricultural supply chain carbon efficiency. The findings offer valuable insights for policymakers and managers in agricultural supply chain enterprises seeking to transition towards low-carbon agriculture.

Towards a transformation approach from SecureUML to ‎Alloy

In critical information systems, Role-Based Access Control (RBAC) has been frequently used. The specification of access control policies is an essential step in securing information systems. This article presents a transformative approach for addressing design challenges within these specifications using Model-Driven Architecture (MDA). A method is introduced for specifying and verifying model-based access control (RBAC) rules by combining SecureUML and Alloy, referred to as SecureUML2Alloy. This approach leverages transformation techniques based on metamodels to express the SecureUML source model in the Alloy target model. The SecureUML2Alloy framework allows for the generation of an initial Alloy code that can be extended with facts, functions, and predicates depending on the case. The transformative framework offers a reliable, automated mechanism for verifying security specifications in critical systems, promoting both theoretical and practical advancements in model transformation and formal verification. This method provides a pathway for future automation tools in secure system design and analysis, contributing to the reliability and security of software systems across multiple fields.

Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Abstract Introduction/Objective The six sigma metric is used to assess the performance of a measurement procedure in comparison to the medical requirement. In clinical laboratories, it can be used to assess the quality of instruments or processes. This study aims to evaluate the performance of hematology analyzers using the six sigma scale. Methods/Case Report This is a descriptive cross-sectional study wherein quality control data from hematology analyzers were obtained. The mean, standard deviation, coefficient of variation, bias and sigma value of the RBC, WBC, hemoglobin, hematocrit, and platelet parameters were determined and the performance of Beckman Coulter DxH 900 was assessed by plotting a normalized OPSpecs chart. Results (if a Case Study enter NA) The WBC and platelet parameters had sigma values of more than 6. Less stringent quality control measures could be applied these parameters which include fewer Westgard rules and fewer control runs. The RBC and hemoglobin parameters had sigma values of at least 3. The hematocrit parameter had a sigma value of less than 3. Improving this parameter could be done by performing optical channel evaluation, using multiple Westgard control rules, increasing the number of control materials and the number of runs. Conclusion This study has demonstrated the analytical performance of hematology analyzers on the six sigma scale, which in turn allows for the selection of the appropriate statistical quality control rule. Ultimately, statistical quality control is only one part of a laboratory’s quality management system. The sigma quality of the testing process, as well as choosing the appropriate control rules and number of controls, may provide the basis for the total quality control strategy of the laboratory. Other pre-analytic and post-analytic factors must also be considered in formulating a laboratory total quality control plan. This would allow the laboratory to provide the best possible quality of services to its clients by increasing efficiency, reducing wastage, and incorporating continuous changes to improve the system.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

The procedure for selecting declarations for NACP’s full audit: novelties and challenges

Every year, from 600 to 900 thousand declarations are submitted to the Unified State Register of Declarations of Persons Authorized to Perform State or Local Governance Functions. Meanwhile, only 27 employees of the National Agency on Corruption Prevention (NACP) are responsible for conduction the full audit of these declarations. In such circumstances, the question of methodology under which the NACP selects declarations for full audit comes to the fore. Prior to the full-scale invasion on Ukrainian territory, national legislation had several provisions that defined the procedure for selecting declarations for full audit. The selection system in place at the time resulted in most full audits being conducted only on a very limited number of top officials. During martial law, in September 2023 - June 2024 the system of selecting declarations for full audit was significantly changed. Inter alia, the NACP adopted three new Procedures to regulate these issues. According to the updated regulations, all submitted declarations undergo a computerized risk assessment, after which declarations with the lowest risk are sent for automated audit, and the declarations with the highest risk are selected for full audit according to four levels of priority. While the new system of selection is certainly a significant step forward, it is not without drawbacks. Firstly, it involves unreasonable concealment of the rules of logical and arithmetic control (LAC) from the public, i.e. the criteria by which the NACP calculates the risk rating of each submitted declaration. Secondly, the institution of automated audit introduced in November 2023 is currently of low practical value and in some cases may even hinder the effective selection of declarations for full audit. To address these shortcomings, the NACP is recommended to make the LAC rules publicly available and provide an opportunity for their public discussion, as well as to abandon the mechanism of automated audit in the form in which it currently exists.

Developing Secure and Interoperable Health Information Systems Using Blockchain Technology to Enhance Data Privacy, Security, and Accessibility in Healthcare

Healthcare information systems are going through a huge change that will make them more efficient, safe, and easy to use. As more persistent records are digitized and private therapeutic information is shared, it is more critical than ever to create beyond any doubt that security, security, and availability are well secured. Blockchain innovation can be a great way to bargain with these issues since it makes a decentralized, unchangeable record that can be utilized to securely store and send healthcare information whereas securing its security and security. This exposition looks at how blockchain innovation might offer assistance make wellbeing data frameworks more secure and more congruous with each other. Healthcare companies can set up a trusted and unchangeable way to oversee understanding information over different frameworks and parties by utilizing blockchain independent plan, cryptography, and voting instruments. One of the leading things approximately blockchain innovation is that it can make data more private. Cryptographic instruments, like encryption and computerized marks, make it conceivable to store and share private wellbeing information securely with individuals who are permitted to see it, without compromising persistent security. Moreover, since blockchain is unchangeable, once information is recorded, it can't be changed or eradicated. This makes the history of who gotten to and changed information clear and simple to check. In conjunction with ensuring protection, blockchain innovation moreover makes things more secure by bringing down the chances of information spills and unlawful get to. Blockchain-based wellbeing data frameworks make information keeping less centralized and use solid encryption strategies to lower the chances of single focuses of failure and unlawful changes. This makes information more secure and dependable. Keen contracts on blockchain stages can too mechanize and execute get to control rules, making beyond any doubt that as it were individuals who are permitted to can see certain restorative data or do certain activities that have as of now been set up. Interoperability is another important part of modern healthcare information systems that lets different healthcare companies and systems work together and share data easily.

Seamless shifting and suppressing clutch judder of 2-speed DCT for EV by deep reinforcement learning

One of the challenges facing widespread adoption of electric vehicles (EVs) is their short driving range. To address this challenge, the development of various EV transmissions is underway. In transmissions, clutches are used for disconnection from the drive source, and a phenomenon called judder, which is a violent vibration, may occur when the clutch slides on a frictional surface. To resolve this problem, the use of deep reinforcement learning, which is being used and advanced in areas such as machine control in simulations, was focused upon. The purpose of this study is to suppress clutch judder using deep reinforcement learning. The deep reinforcement learning model was developed for seamless gear shift control, and the gearshift results without control were compared with the gearshift results after training. As a result, a control rule that achieves seamless gear shifting while suppressing the judder is designed by applying deep reinforcement learning to gear shifting control. In addition, seamless gear shifting can be achieved for various patterns of friction coefficients, enabling the development of a robust controller for changing the friction coefficients.

Implementation of Skogestad’s Internal Model Control Rule in Tuning Proportional-Integral Controllers for Two-Input and Two-Output Systems Considering Individual Controller Performance Trade-off

Abstract In industrial plants, tuning of decentralized Proportional-Integral-Derivative feedback (PID) controllers in Multiple-Input and Multiple-Output (MIMO) systems is a complex problem, due to input and output interactions, that must be done to ensure individual control objectives are met, while system being sufficiently robust. In this study, a two-input and two-output (TITO) PI controller tuning algorithm was developed considering controller performance prioritization given performance trade-offs, expressed as the novel Individual Controller Performance Trade-off Coefficient (ICPTC), and system robustness, expressed as sensitivity peaks from the generalized Nyquist stability plot. Simplified effective open-loop transfer functions (EOTFs) of the two input-output channels were derived and used along with Skogestad’s Internal Model Control (SIMC) to calculate PI controller gains. Extensive simulations using Wood-Berry (WB) and Vinante-Luyben (VL) distillation column models were made to observe how the sensitivity peaks behave across the simulations, which was then used as the basis of the developed tuning algorithm. A simplified tuning algorithm, which requires less information compared to the main algorithm, was also derived. A mapping algorithm was also created, allowing specific controller gains from other studies to be mapped to an equivalent set of controller gains in the algorithm’s search field. The effectiveness of the three algorithms were tested using Wardle-Wood distillation column (WW) and Xiong and Cai (XC) process models. It was demonstrated using the main algorithm that there is a trade-off between controller performances when ICPTC is varied, at different values of design sensitivity peaks between 1.96 to 4. The usefulness of the simplified tuning and mapping algorithms were also illustrated in the study. This study is potentially useful in tuning TITO PI controllers in industrial plants, where meeting individual control objectives while maintaining robustness is more important than optimizing overall control performance.

Co-optimization on Ecological Adaptive Cruise Control and Energy Management of Automated Hybrid Electric Vehicles

Electrified drive systems and eco-driving technologies play a crucial role in promoting energy conservation. Eco-driving for hybrid electric vehicles(HEVs) is an intricate problem involving intertwined speed planning and energy management. In this context, an ecological adaptive cruise control (eco-ACC) and powertrain energy management strategy considering Signal Phase Timing Message (SPaT) can enhance both performance and real-time implementation. Specifically, this study develops a novel co-optimization method based on Pontryagin's minimum principle (PMP) that combines car-following control rules with the SPaT for a parallel HEV. The methodology involves the following steps: firstly, the parallel HEV model is established; secondly, the safe following distance model is constructed and the car-following control rules are devised to ensure safe driving. Subsequently, the co-optimization method based on PMP is then presented to simultaneously optimize the eco-driving problem of an ego-vehicle by converting the inter-vehicle distance constraint of the lead-vehicle into the limitation of the speed of the ego-vehicle. Finally, simulations are conducted under different scenarios for both fused SPaT and non-fused SPaT strategy. The simulation results demonstrate a reduction in fuel consumption by 6.27% and 5.69% in two different scenarios, respectively, and a shorter driving time for the fused SPaT strategy compared to the non-fused SPaT strategy.

Study on mixed traffic characteristics around highway on-ramp bottleneck using a microscopic simulation model

In the early mixed traffic environment, connected and automated vehicles (CAVs) tend to follow simple control rules for the sake of traffic safety and stability. Comprehensive analysis of overall mixed traffic performance and vehicle to vehicle interactions around highway on-ramp bottleneck is a basis for further traffic management and control. And yet CAVs in existing researches are mainly in the form of cooperative vehicular platoon, and mixed traffic scenarios within simulation software are not applicable for in-depth investigation. The purpose of this paper is to develop a microscopic simulation model to study in detail lane-based mixed traffic performance around the highway on-ramp. This model firstly considers vehicle characteristics of individual human vehicles (HVs) and CAVs by combining different microscopic traffic models. After the hypothetical on-ramp bottleneck structure is constructed, various simulation scenarios are developed under varying traffic volumes and changing market penetration rates (MPRs) of CAVs. Simulation results show that mixed traffic flow is improved in terms of congestion patterns and traffic capacity. For oscillating and nearly homogeneous congested traffic, congestion degrees are greatly reduced when CAV MPR above 30 % and 50 % respectively. Mixed traffic capacity is increased by 19.3 % when a smaller desired time gap set for CAVs, while it shows no significant change in a larger value. In the HV-HV interactions, traffic conflicts are slightly influenced by CAVs. Comparatively, conflict situations in HV-CAV interactions get worse, the proportion of time to collision (TTC) greater than 2 s monotonically declining with the rise in CAV MPR. It is concluded that traffic characteristics overall show a good momentum when MPR of CAV and the desired time gap of CAVs in mixed traffic are appropriate.

Identifying limit reference points for robust harvest control rules in fisheries management

Risk and uncertainty are intrinsic characteristics of natural resources that must be taken into account in their management. Harvest control rules (HCR) used to be the central management tool to control stock fisheries in an uncertain context. A typical HCR determines fishing mortality as a linear relationship of the biomass binding only when the biomass is above a critical risk value. Choosing the linear relationship and the risk value is a complex task when there is uncertainty because it requires a high level of data and an in-deep knowledge of the stock. This paper fully characterizes robust HCRs that explicitly include scientific uncertainty using the robust control theory approach. Our theoretical findings show that under uncertainty: i) Constant HCRs are not robust; ii) Robust HCRs show a steeper linear relationship between fishing mortality and biomass and a higher value of biomass to be consider at risk than non-robust HCRs. From the implementation viewpoint, we assume a three-sigma rule and show that robustness is achieved by selecting a fishing mortality such that its deviation from the fishing mortality target is twice the deviation of the biomass from the biomass target, and the critical value of the biomass (the point below which fishing should cease, or become as close to zero as possible) is half of the biomass associated with the maximum sustainable yield when this is the target.

Anti Wind‐Up and Robust Data‐Driven Model‐Free Adaptive Control for MIMO Nonlinear Discrete‐Time Systems

ABSTRACTThis article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC.

Adaptive Operating Rules for Flood Control of a Multi-Purpose Reservoir

Almost all multipurpose reservoirs in Romania were put into operation 30–50 years ago or even earlier. Meanwhile, a large volume of hydrologic data has been collected, and the initial design flood should be reconsidered. Consequently, the operating rules of flow control structures (bottom gates and weir gates) should be re-examined, mainly for medium and low-frequency floods. The design flood is not unique, being characterized by different shapes and time to peak, which has consequences for flood mitigation rules. Identifying the critical design flood is an important preliminary step, although it is usually neglected in flood management. Simulating the operation of the Stânca–Costești reservoir on the Prut River, it was found that the design flood corresponding to the maximum value of the compactness coefficient is the most difficult to mitigate considering the specific conditions of the dam and the reservoir: the prescribed conservation level in the reservoir, and the design flood volume of medium and rare floods that far exceeds the flood control volume. These conditions can jeopardize both dam safety and downstream flood protection. The main steps of the proposed approach are as follows: (1) developing the hydraulic model; (2) statistical processing of the registered floods and defining critical design floods for different AEPs (Annual Exceedance Probabilities); (3) deriving optimal operation rules based on a simulation-optimization model; (4) implementing real-time adaptive operation of the mechanical outlets; and (5) critically assessing the operating rules after the event. Based on the hydrological forecast, if necessary, new outlets are put into operation while keeping the ones already activated. Based on the hydrological forecast and properly operated, the safety of the Stânca–Costești dam is guaranteed even in the event of a 0.1% CC (Climate Change) flood. However, for floods greater than 1% magnitude, the carrying capacity of the downstream riverbed is exceeded. The main gaps addressed in this paper are the following: (1) the establishment of critical design floods, and (2) the adaptive operating rules of outlet devices aimed at optimizing flood control results, using short-term flood forecasts.