Search Results (30)

This research proposes a fractional-order adaptive neural control scheme using an optimized backstepping (OB) approach to address strict-feedback nonlinear systems with uncertain control directions and predefined performance requirements. The OB framework integrates both fractional-order virtual and actual controllers to achieve global optimization, while a Nussbaum-type function is introduced to handle unknown control paths. To ensure convergence to desired accuracy within a prescribed time, a fractional-order dynamic-switching mechanism and a quartic-barrier Lyapunov function are employed. An input-to-state practically stable (ISpS) auxiliary signal is designed to mitigate unmodeled dynamics, leveraging classical lemmas adapted to fractional-order systems. The study further investigates a decentralized control scenario for large-scale stochastic nonlinear systems with uncertain dynamics, undefined control directions, and unmeasurable states. Fuzzy logic systems are employed to approximate unknown nonlinearities, while a fuzzy-phase observer is designed to estimate inaccessible states. The use of Nussbaum-type functions in decentralized architectures addresses uncertainties in control directions. A key novelty of this work lies in the combination of fractional-order adaptive control, fuzzy logic estimation, and Nussbaum-based decentralized backstepping to guarantee that all closed-loop signals remain bounded in probability. The proposed method ensures that system outputs converge to a small neighborhood around the origin, even under stochastic disturbances. The simulation results confirm the effectiveness and robustness of the proposed control strategy. Full article

Traffic congestion at a single intersection can propagate and thus affect adjacent intersections as well, potentially resulting in prolonged gridlock across an entire urban area. Despite numerous research efforts aimed at developing intelligent traffic signal control systems, urban areas continue to experience traffic congestion. This paper presents a novel agent-based fuzzy traffic control system for multiple road intersections. The proposed system is designed to operate in a decentralized manner, with each intersection having its own agent (fuzzy controller) functioning concurrently. The intelligent fuzzy controller of the system can recognize emergency vehicles, assess the queue length and waiting time of vehicles, measure the distance of vehicles from intersections, and consider the cumulated waiting times of short vehicle queues. Two distinct types of agent-based intelligent fuzzy traffic control systems were implemented for comparison: one involving collaboration between an agent and its immediate neighboring agent(s) (where one intersection exchanges traffic data with its immediate neighboring intersection(s)), and the other implementing a non-collaborative agent-based intelligent fuzzy traffic control system (where the individual intersection has no direct communication). Following the experimental simulations, the results were compared with those of existing intelligent fuzzy traffic control systems that lack any module to calculate the distance of the vehicles from the intersection. The results demonstrated that the proposed agent-based system of controllers exhibited superior performance compared with the existing fuzzy controllers in terms of indicators such as average waiting time, fuel consumption, and CO₂ emissions. For instance, the proposed system reduced the average waiting time of vehicles at an intersection by 48.65% compared with the existing three-stage intelligent fuzzy traffic control system. In addition, a comparison was conducted between non-collaborating and collaborating agent-based intelligent fuzzy traffic control systems, where collaboration achieved better results than the non-collaborating system. In the simulation experiments, an interesting new feature emerged: despite any direct communication missing at multiple intersections, green waves evolved with time. This emergent feature suggests that fuzzy controllers have the potential to evolve and adapt to traffic complexity issues in urban environments when operating in an autonomous agent-based mode. This study demonstrates that agent-based fuzzy controllers can effectively communicate with one another to share traffic data and improve the overall system performance. Full article

Searchable encryption (SE) allows users to efficiently retrieve data from encrypted cloud data, but most of the existing SE solutions only support precise keyword search. Fuzzy searchable encryption agrees with practical situations well in the cloud environment, as search keywords that are misspelled to some extent can still generate search trapdoors that are as effective as correct keywords. In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. For example, in medical application scenarios where malicious cloud servers may exist, diverse types of files need to correspond to doctors in the corresponding departments, and there is a lack of fine-grained access control for sharing decryption keys for different types of files. In the application of medical cloud storage, malicious cloud servers may return incorrect ciphertext files. Since diverse types of files need to be guaranteed to be accessible by doctors in the corresponding departments, sharing decryption keys with the corresponding doctors for different types of files is an issue. To solve these problems, a verifiable fuzzy searchable encryption with blockchain-assisted multi-user scenarios is proposed. Locality-sensitive hashing and bloom filters are used to realize multi-keyword fuzzy search, and the bigram segmentation algorithm is optimized for keyword conversion to improve search accuracy. To realize fine-grained access control in multi-user scenarios, ciphertext-policy attribute-based encryption (CP-ABE) is used to distribute the shared keys. In response to the possibility of malicious servers tampering with or falsifying users’ search results, the scheme leverages the blockchain’s technical features of decentralization, non-tamperability, and traceability, and uses smart contracts as a trusted third party to carry out the search work, which not only prevents keyword-guessing attacks within the cloud server, but also solves the verification work of search results. The security analysis leads to the conclusion that the scheme is secure under the adaptively chosen-keyword attack. Full article

In large buildings, effective load shedding and shifting and providing the maximum power through solar renewable sources remain challenges because of users’ unpredictable load consumption. Conventionally, load shifting, load shedding, and load covering are majorly dependent on user inputs. The lack of user interest in participating in demand responses for effective load shifting and covering remains a problem. Effective load covering through renewables and user-friendly load shedding and shifting with maximized user participation are challenging and demand high-resolution user load consumption information, which are not possible without sophisticated communication and digital twins. In this research work, a novel fuzzy-logic-based cascaded decentralized load-controlling mechanism has been developed that manages the residential building load through load-shifting, load-covering, and load-shedding schemes without any communication protocols and digitization between residential units. The decentralized controller aims to effectively utilize the centralized resources of power generation with the effective automated participation of users. The quantification of the load shifting, covering, and shedding performed during peak hours was well covered under the load-covering scheme, and the results showed that flexibility capacities of 1617 kW were achieved for load covering, 294 kW for load shedding, and 166.34 kW through shifting. A total load of 60 kW, which was reduced during shedding and shifting, was well covered during load covering through renewables. Full article

Investing in green hydrogen systems has become a global objective to achieve the net-zero emission goal. Therefore, it is seen as the primary force behind efforts to restructure the world’s energy, lessen our reliance on gas, attain carbon neutrality, and combat climate change. This paper proposes a power management for a net zero emission PV microgrid-based decentralized green hydrogen system. The hybrid microgrid combines a fuel cell, battery, PV, electrolyzer, and compressed hydrogen storage (CHSU) unit aimed at power sharing between the total components of the islanded DC microgrid and minimizing the equivalent hydrogen consumption (EHC) by the fuel cell and the battery. In order to minimize the EHC and maintain the battery SOC, an optimization-based approach known as the Equivalent Consumption Minimization Strategy (ECMS) is used. A rule-based management is used to manage the power consumed by the electrolyzer and the CHSU by the PV system in case of excess power. The battery is controlled by an inverse droop control to regulate the dc bus voltage and the output power of the PV system is maximized by the fuzzy logic controller-based MPPT. As the hybrid microgrid works in the islanded mode, a two-level hierarchical control is applied in order to generate the voltage and the frequency references. The suggested energy management approach establishes the operating point for each system component in order to enhance the system’s efficiency. It allows the hybrid system to use less hydrogen while managing energy more efficiently. Full article

This paper investigates the decentralized fuzzy control problems for nonlinear-state-unmeasured interconnected descriptor systems (IDSs) that utilize the observer-based-feedback approach and the proportional–derivative feedback control (PDFC) method. First of all, the IDS is represented as interconnected Takagi–Sugeno (T–S) fuzzy subsystems. These subsystems can effectively capture the dynamic behavior of the system through fuzzy rules. For the stability analysis of the system, this paper uses the free-weighing Lyapunov function (FWLF), which allows the designer to set the weight matrix, to achieve the desired control performance and design the controller more easily. Furthermore, the control problem can be transformed into a set of linear matrix inequalities (LMIs) through the Schur complement, which can be solved using convex optimization methods. Simulation results confirm the effectiveness of the proposed method in achieving the desired control objectives and ensuring system stability. Full article

The conventional point-estimate human health risk assessment (HHRA) primarily uses average concentrations of a limited number of samples due to the high monitoring costs of heavy metals in groundwater. The results can be erroneous when concentrations significantly deviate from the average across the collected samples in an investigation region. The present research developed a hierarchical fuzzy-based HHRA (F-HHRA) framework to handle variations in limited data sets and subjectively established a broader range of risks for various exposure groups. Groundwater samples from 80 to 120 m deep in shallow wells were collected from agricultural farms along Wadi Rumah in the Qassim Region of Saudi Arabia. Laboratory testing found total dissolved solids much higher than the promulgated drinking water quality standards. As the aftertaste issue eliminated the raw water potability, the study considered dermal exposure for HHRA. The collected samples were tested for thirteen potential heavy metals (HMs), including barium (Ba), boron (B), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), lithium (Li), manganese (Mn), silver (Ag), strontium (Sr), thallium (TI), and zinc (Zn). Cu, Fe, Pb, Ag, and TI were lower than the detectable limit of the inductively coupled plasma mass spectrometry device. Concentrations of the remaining HMs in wastewater outfalls that were much less than the groundwater eradicated the impact of anthropogenic activities and affirmed natural contamination. Apart from 10% of the samples for Mn and 90% of the samples for Sr, all the other HMs remained within the desired maximum allowable concentrations. Point-estimate and fuzzy-based approaches yielded ‘low’ dermal non-cancer risk and cancer risk for all groups other than adults, where dermal cancer risk of Cr remained in the ‘acceptable’ (1 × 10⁻⁶ and 1 × 10⁻⁵) risk zone. Although dermal risk does not require controls, scenario analysis established the rationality of F-HHRA for more contaminated samples. The proposed hierarchical F-HHRA framework will facilitate the decision-makers in concerned agencies to plan risk mitigation strategies (household level and decentralized systems) for shallow well consumers in Saudi Arabia and other arid regions. Full article

Fixed-wing Unmanned Aerial Vehicles (UAVs) cannot fly at speeds lower than critical stall speeds. As a result, hovering during a potential collision scenario, like with rotary-wing UAVs, is impossible. Moreover, hovering is not an optimal solution for Collision Avoidance (CA), as it increases mission time and is innately fuel-inefficient. This work proposes a decentralized Fuzzy Inference System (FIS)-based resolution algorithm that modulates the point-to-point mission path while ensuring the continuous motion of UAVs during CA. A simplified kinematic guidance model with coordinated turn conditions is considered to control the UAVs. The model employs a proportional-derivative control of commanded airspeed, bank angle, and flight path angle. The commands are derived from the desired path, characterized by airspeed, heading, and altitude. The desired path is, in turn, obtained using look-ahead points generated for the target point. The FIS aims to mimic human behavior during collision scenarios, generating modulation parameters for the desired path to achieve CA. Notably, it is also scalable, which makes it easy to adjust the algorithm parameters, as per the required missions, and factors specific to a given UAV. A genetic algorithm was used to optimize FIS parameters so that the distance traveled during the mission was minimized despite path modulation. The proposed algorithm was optimized using a pairwise conflict scenario. The effectiveness of the algorithm was evaluated through a Monte Carlo simulation of random conflict scenarios involving multiple UAVs operating in a confined space. Full article

Knowledge-Defined Networking (KDN) necessarily consists of a knowledge plane for the generation of knowledge, typically using machine learning techniques, and the dissemination of knowledge, in order to make knowledge-driven intelligent network decisions. In one way, KDN can be recognized as knowledge-driven Software-Defined Networking (SDN), having additional management and knowledge planes. On the other hand, KDN encapsulates all knowledge-/intelligence-/ cognition-/machine learning-driven networks, emphasizing knowledge generation (KG) and dissemination for making intelligent network decisions, unlike SDN, which emphasizes logical decoupling of the control plane. Blockchain is a technology created for secure and trustworthy decentralized transaction storage and management using a sequence of immutable and linked transactions. The decision-making trustworthiness of a KDN system is reliant on the trustworthiness of the data, knowledge, and AI model sharing. To this point, a KDN may make use of the capabilities of the blockchain system for trustworthy data, knowledge, and machine learning model sharing, as blockchain transactions prevent repudiation and are immutable, pseudo-anonymous, optionally encrypted, reliable, access-controlled, and untampered, to protect the sensitivity, integrity, and legitimacy of sharing entities. Furthermore, blockchain has been integrated with knowledge-based networks for traffic optimization, resource sharing, network administration, access control, protecting privacy, traffic filtering, anomaly or intrusion detection, network virtualization, massive data analysis, edge and cloud computing, and data center networking. Despite the fact that many academics have employed the concept of blockchain in cognitive networks to achieve various objectives, we can also identify challenges such as high energy consumption, scalability issues, difficulty processing big data, etc. that act as barriers for integrating the two concepts together. Academicians have not yet reviewed blockchain-based network solutions in diverse application categories for diverse knowledge-defined networks in general, which consider knowledge generation and dissemination using various techniques such as machine learning, fuzzy logic, and meta-heuristics. Therefore, this article fills a void in the content of the literature by first reviewing the diverse existing blockchain-based applications in diverse knowledge-based networks, analyzing and comparing the existing works, describing the advantages and difficulties of using blockchain systems in KDN, and, finally, providing propositions based on identified challenges and then presenting prospects for the future. Full article

This paper proposes a decentralized robust constrained cost fuzzy controller (DRCCFC) design for nonlinear descriptor interconnected systems (DIS) with uncertainties. The considered nonlinear DIS is modeled using Takagi–Sugeno fuzzy model (T-S FM) with fuzzy rules and strong interconnections. To derive sufficient stability conditions, the quadratic Lyapunov function (QLF) and free-weighting function (FWF) are defined. In contrast to the existing control approaches, the proportional–derivative feedback (PDF) control is introduced in this paper. Using the PDF control techniques, the regular and causal problems of the system can be solved easily. Based on the PDF control technique and constrained cost control (CCC) function, a set of fuzzy controllers are designed to effectively control the Takagi–Sugeno descriptor interconnected systems (T-S DIS). Then, the proposed sufficient conditions for the T-S DIS are derived in the form of linear matrix inequalities using the Schur complement technique. Finally, two simulation examples are provided to demonstrate the validity of the proposed control scheme. Full article

Electric vehicles (EVs) are considered as the leading-edge form of mobility. However, the integration of electric vehicles with charging stations is a contentious issue. Managing the available grid power and bus voltage regulation is addressed through renewable energy. This work proposes a grid-connected photovoltaic (PV)-powered EV charging station with converter control technique. The controller unit is interfaced with the renewable energy source, bidirectional converter, and local energy storage unit (ESU). The bidirectional converter provides a regulated output with a fuzzy logic controller (FLC) during charging and discharging. The fuzzy control is implemented to maintain a decentralized power distribution between the microgrid DC-link and ESU. The PV coupled to the DC microgrid of the charging station is variable in nature. Hence, the microgrid-based charging is examined under a range of realistic scenarios, including low, total PV power output and different state of charge (SOC) levels of ESU. In order to accomplish the effective charging of EV, a decentralized energy management system is created to control the energy flow among the PV system, the battery, and the grid. The proposed controller’s effectiveness is validated using a simulation have been analyzed using MATLAB under various microgrid situations. Additionally, the experimental results are validated under various modes of operation. Full article

In this work, we train controllers (models) using Genetic Fuzzy Methodology (GFM) for learning cooperative behavior in a team of decentralized UAVs to transport a shared slung payload. The training is done in a reinforcement learning fashion where the models learn strategies based on feedback received from the environment. The controllers in the UAVs are modeled as fuzzy systems. Genetic Algorithm is used to evolve the models to achieve the overall goal of bringing the payload to the desired locations while satisfying the physical and operational constraints. The UAVs do not explicitly communicate with one another, and each UAV makes its own decisions, thus making it a decentralized system. However, during the training, the cost function is defined such that it is a representation of the team’s effectiveness in achieving the overall goal of bringing the shared payload to the target. By including a penalization term for any constraint violation during the training, the UAVs learn strategies that do not require explicit communication to achieve efficient transportation of payload while satisfying all constraints. We also present the performance metrics by testing the trained UAVs on new scenarios with different target locations and with different number of UAVs in the team. Full article

This article addresses the decentralized multi-performance (MP) fuzzy control problem of nonlinear large-scale descriptor (LSD) systems. The considered LSD system contains several subsystems with nonlinear interconnection and external disturbances, and the Takagi–Sugeno fuzzy model (TSFM) is adopted to represent each nonlinear subsystem. Based on the proportional-plus-derivative state feedback (PDSF) scheme, we aim to design a decentralized MP fuzzy controller that guarantees the stabilization, mixed H_∞, and passivity performance control (MHPPC), and the guaranteed cost control (GCC) performance of the closed-loop Takagi–Sugeno LSD (TSLSD) systems. Furthermore, we introduce the Lyapunov stability theory and the free-weighting matrix scheme to analyze the stability of the TSLSD system. The proposed sufficient conditions can be transformed as linear matrix inequality (LMI) forms through Schur’s complement, which can be easily solved with the LMI Toolbox. Finally, to illustrate the proposed approach, two examples and simulation results are presented. Full article

This work proposes the use of fuzzy-logic-based voltage frequency control (VFC) and adaptive inertia to improve the frequency response of a virtual synchronous generator (VSG)-based isolated microgrid system. The joint VFC and inertial control scheme is proposed to limit frequency deviations in these isolated microgrid systems, mainly caused by the increasing penetration of intermittent distributed energy resources, which lack rotational inertia. The proposed controller uses artificial neural networks (ANN) to estimate the exponent of voltage-dependent loads and modulate the system frequency by adjusting the output voltage of the VSGs, which increases the system’s active power reserves while providing inertial control by adjusting the inertia of VSGs to minimize frequency and VSG DC-link voltage excursions. A genetic algorithm (GA)-based optimization strategy is developed to optimally adjust the parameters of the fuzzy logic controller to diminish the impact of disturbances on the system. In addition, the proposed technique is illustrated through simulations within the framework of a test system based on the CIGRE medium-voltage benchmark under various circumstances. The results of these simulations demonstrate that the proposed control strategy outperforms existing methods. Full article

This study deals with the decentralized sampled-data fuzzy tracking control of a quadrotor unmanned aerial vehicle (UAV) considering the communication delay of the feedback signal. A decentralized Takagi–Sugeno (T–S) fuzzy approach is adopted to represent the quadrotor UAV as two subsystems: the position control system and the attitude control system. Unlike most previous studies, a novel decentralized controller considering the communication delay for the position control system is proposed. In addition, to minimize the increase in computational complexity, the Lyapunov–Krasovskii functional (LKF) is configured as the only state required for each subsystem. The design conditions guaranteeing the tracking performance of the quadrotor UAV are derived as linear matrix inequalities (LMIs) that are numerically solved. Lastly, the validity of the proposed design method is verified by comparing the results through simulation examples with and without communication delay. Full article