Search Results (53)

Ensuring reliable power system control demands innovative architectural solutions. This research introduces a fault-tolerant hybrid parallel compensator architecture for load frequency control (LFC), combining a Proportional–Integral–Derivative with Filter (PIDF) compensator with a Fuzzy Fractional-Order PI-PD (Fuzzy FOPI–FOPD) module. Particle Swarm Optimization (PSO) determines optimal PID gains, while the Catch Fish Optimization Algorithm (CFOA) tunes the Fuzzy FOPI–FOPD parameters—both minimizing the Integral Time Absolute Error (ITAE) index. The parallel compensator structure guarantees continuous operation during subsystem faults, substantially boosting grid reliability. Rigorous partial failure tests confirm uncompromised performance-controlled degradation. Benchmark comparisons against contemporary controllers reveal the proposed architecture’s superiority, quantifiable through transient metric enhancements: undershoot suppression (−9.57 × 10⁻⁵ p.u. to −1.17 × 10⁻⁷ p.u.), settling time improvement (8.8000 s to 3.1511 s), and ITAE reduction (0.0007891 to 0.0000001608), verifying precision and stability gains. Resilience analyses across parameter drift and step load scenarios, simulated in MATLAB/Simulink, demonstrate superior disturbance attenuation and operational stability. These outcomes confirm the solution’s robustness, dependability, and field readiness. Overall, this study introduces a transformative LFC strategy with high practical viability for modern power networks. Full article

To effectively deal with the uncertainty of value assessments of industry-characteristic universities, this paper proposes a new fuzzy multi-attribute assessment method. Firstly, we define the complex cubic fractional orthotriple fuzzy set (CCFOFS) for expressing ambiguous information and present some basic operational rules and information measures. Then, we present the complex cubic fractional orthotriple fuzzy Dombi-weighted power-partitioned Muirhead mean (CCFOFDWPPMM) operator, which combines the superiority of the Dombi operations, power average (PA) operator, and partitioned Muirhead mean (PMM) operator. Further, a multi-attribute assessment method is constructed based on the CCFOFDWPPMM operator and the Integrated Determination of Objective Criteria Weights (IDOCRIW) method. Furthermore, we constructed a novel assessment index system for the construction of first-class disciplines. Finally, this paper verifies the validity and applicability of the method by applying the novel multi-attribute assessment method to a practical case of first-class discipline construction in industry-characteristic universities. Full article

In the integrated hydro–wind–solar–storage system, the strong output fluctuations of wind and solar power, along with prominent system nonlinearity and time-varying characteristics, make it difficult for traditional PID controllers to achieve high-precision and robust dynamic control. This paper proposes a fuzzy fractional-order PID control strategy based on a multi-objective optimization algorithm, aiming to enhance the system’s frequency regulation, power balance, and disturbance rejection capabilities. The strategy combines the adaptive decision-making ability of fuzzy control with the high-degree-of-freedom tuning features of fractional-order PID. The multi-objective optimization algorithm AGE-MOEA-II is employed to jointly optimize five core parameters of the fuzzy fractional-order PID controller (K_p, K_i, K_d, λ, and μ), balancing multiple objectives such as system dynamic response speed, steady-state accuracy, suppression of wind–solar fluctuations, and hydropower regulation cost. Simulation results show that compared to traditional PID, single fractional-order PID, or fuzzy PID controllers, the proposed method significantly reduces system frequency deviation by 35.6%, decreases power overshoot by 42.1%, and improves renewable energy utilization by 17.3%. This provides an effective and adaptive solution for the stable operation of hydro–wind–solar–storage systems under uncertain and variable conditions. Full article

The assessment of grid-connected systems depends on their cost efficiency, reliability, and greenhouse gas (GHG) reduction potential. This study presents a multi-objective optimization framework for designing a grid-connected photovoltaic (PV) and battery energy storage (BES) system integrated with an electric vehicle (EV) for a household in Riyadh, Saudi Arabia. The framework aims to minimize the Cost of Energy (COE) and Loss of Power Supply Probability (LPSP) while maximizing the Renewable Energy Fraction (REF). Additionally, GHG emissions are evaluated as a result of these objectives. The EV operates in Vehicle-to-Home (V2H) mode, enhancing system flexibility and energy management. The optimization process employs two advanced metaheuristic techniques, Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Harris Hawks Optimization (MOHHO), to identify Pareto front solutions. Fuzzy logic is then applied to determine a balanced compromise among the economically optimal (minimum COE), renewable energy-oriented (maximum REF), and environmentally optimal (minimum GHG emissions) solutions. Simulation results show that the proposed system achieves a COE of USD 0.0554/kWh, a LPSP of 1.96%, and an REF of 92.55%. Although the COE is slightly higher than that of the grid, the system provides significant environmental and renewable energy benefits. This study highlights the potential of integrating dynamic EV management and advanced optimization techniques to enhance the performance of grid-connected systems. The findings demonstrate the effectiveness of combining Pareto-based optimization with fuzzy logic to achieve balanced solutions addressing economic, environmental, and renewable energy objectives, paving the way for sustainable energy systems in urban households. Full article

Load frequency control (LFC) remains a critical challenge in ensuring the stability of modern power grids. The integration of nonlinear dynamics into LFC design is paramount to achieving robust performance, which directly underpins grid reliability. This study introduces a novel hybrid control strategy—a fuzzy fractional-order proportional–integral–derivative (Fuzzy FOPID) controller augmented with a proportional–integral (PI) compensator—for LFC applications in two distinct dual-area interconnected power systems. To optimize the controller’s parameters, the recently developed Catch Fish Optimization Algorithm (CFOA) is employed, leveraging the Integral Time Absolute Error (ITAE) as the primary cost function for precision tuning. A comprehensive comparative analysis is conducted to benchmark the proposed controller against the existing methodologies documented in the literature. Nonlinear elements’ impact on the system stability is also investigated. The investigation evaluates the impact of critical nonlinearities, including governor dead band (GDB) and generation rate constraints (GRCs), on system performance. The simulation results demonstrate that the CFOA-tuned Fuzzy FOPID + PI controller exhibits superior robustness and dynamic response compared to conventional approaches, effectively mitigating frequency deviations and maintaining grid stability under nonlinear operating conditions. Furthermore, the CFOA demonstrates marginally superior convergence and tuning accuracy relative to the widely adopted Particle Swarm Optimization (PSO) algorithm. These findings underscore the proposed controller’s potential as a high-performance solution for real-world LFC systems, particularly in scenarios characterized by nonlinearities and interconnected grid complexities. This study advances the field by bridging the gap between fractional-order fuzzy control theory and practical power system applications, offering a validated strategy for enhancing grid resilience in dynamic environments. Full article

Cardiac pacemakers are standard implantable medical devices that regulate and treat heart rhythm disorders, primarily aiming to improve patient health outcomes. This study presents the systematic design, implementation, and simulation-based validation of a novel fuzzy-immune adaptive Fractional-Order Linear Quadratic Integral (FO-LQI) control strategy for heart rate (HR) regulation using cardiac pacemakers. Unlike the conventional LQI controller, the proposed approach replaces the integer-order integrator with a fractional-order integral operator to enhance the controller’s design flexibility and dynamic response. To address the implementation challenges of fixed fractional exponents, a fuzzy-immune adaptation mechanism is introduced to modulate the fractional order in real time. This adaptive scheme improves the controller’s robustness across varying physiological states, enabling more responsive HR adaptation to the patient’s metabolic demands. The proposed controller is modeled and simulated in MATLAB/Simulink using physiologically relevant test cases. Comparative simulation results show that the fuzzy-immune adaptive FO-LQI controller outperforms the baseline LQI and fixed FO-LQI controllers in achieving time-optimal HR regulation. These findings validate the reliability and enhanced robustness of the proposed control scheme for simulating cardiac behavior under diverse physiological conditions. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

This paper introduces a novel fractional-order fuzzy logic controller (FOFLC) designed for stator voltage control in standalone doubly fed induction generator (DFIG) systems used in wind energy applications. Unlike traditional fuzzy logic controllers (FLCs), which are limited by integer-order dynamics, the FOFLC leverages the advanced principles of fractional-order (FO) calculus. By integrating fuzzy logic with fractional-order operators, the FOFLC enhances system precision, adaptability, and interpretability while addressing the inherent limitations of conventional proportional-integral (PI) controllers and integer-order FLCs. A key innovation of the FOFLC is its dual-mode architecture, enabling it to operate seamlessly as either a traditional FLC or a fractional-order FOFLC controller. This versatility allows for independent tuning of fractional parameters, optimizing the system’s response to transients, steady-state errors, and disturbances. The controller’s flexibility makes it particularly well-suited for nonlinear and dynamically complex stand-alone renewable energy systems. The FOFLC is experimentally validated on a 3-kW DFIG test bench using the dSPACE-1104 platform under various operating conditions. Compared to a conventional PI controller, the FOFLC demonstrated superior performance, achieving 80% reduction in response time, eliminating voltage overshoot and undershoot, reducing stator power and torque ripples by over 46%, and decreasing total harmonic distortion (THD) of both stator voltage and current by more than 50%. These results confirm the FOFLC’s potential as a robust and adaptive control solution for stand-alone renewable energy systems, ensuring high-quality power output and reliable operation. Full article

This article considers a class of Caputo fractional-order fuzzy cellular neural networks (CFOFCNNs) with transmission delays and uncertain perturbations. In particular, nonlinear activations and fuzzy operators AND and OR are investigated in the drive-response neural networks (NNs). To achieve practical finite-time (PFT) synchronization and finite-time (FT) synchronization of the studied systems, we design new nonlinear controllers including four feedback terms in this paper, and each carries a different role in the control process. Integrating different comparison principles and nonlinear feedback schemes, straightforward synchronization criteria of the CFOFCNNs are derived. Unlike existing works, a significant finding is that adjusting the feedback coefficients and parameters can enable synchronization switching. Namely, changing one of the feedback terms from positive to negative can cause PFT synchronization to switch to FT synchronization via adjusted control parameters, making our control methods applicable to different scenarios. The settling time depends explicitly on feedback coefficients, initial conditions, and fractional order. Full article

Wheel loaders play a crucial role in daily production and transportation. With the rapid development of intelligence in passenger vehicles, freeing loader operators from high-risk and repetitive tasks has become a pressing issue. This paper presents a novel and efficient path planning and tracking framework tailored to the unique body structure and specific operating environment of loaders. We improve the Hybrid A* search algorithm based on the operational characteristics of loaders and integrate it with dynamically updated grid maps to enable the autonomous planning of loader operating paths in unstructured environments, meeting the efficiency requirements of production. Additionally, to address the challenge of poor trajectory tracking control accuracy caused by hydraulic articulated steering, we propose a new loader trajectory tracking controller based on the idea of hierarchical control. We use an extended state observer to compensate for unknown disturbances in the steering execution layer and employ fuzzy fractional-order PID to handle the nonlinearity of loaders. Field experiments using the proposed approach demonstrate that loaders can autonomously and in real-time complete tasks in dynamically changing operating scenarios. Full article

This paper presents a novel Fuzzy Logic Controller (FLC) framework aimed at enhancing the performance and stability of Automatic Voltage Regulators (AVRs) in power systems. The proposed system combines fuzzy control theory with the Fractional Order Proportional Integral Derivative (FOPID) technique and employs cascading control theory to significantly improve reliability and robustness. The unique control architecture, termed Fuzzy Fractional Order Proportional Integral (PI) plus Fractional Order Proportional Derivative (PD) plus Integral (Fuzzy FOPI+FOPD+I), integrates advanced control methodologies to achieve superior performance. To optimize the controller parameters, the Teaching–Learning-Based Optimization (TLBO) algorithm is utilized in conjunction with the Integral Time Absolute Error (ITAE) objective function, ensuring precise tuning for optimal control behavior. The methodology is validated through comparative analyses with controllers reported in prior studies, highlighting substantial improvements in performance metrics. Key findings demonstrate significant reductions in peak overshoot, peak undershoot, and settling time, emphasizing the proposed controller’s effectiveness. Additionally, the robustness of the controller is extensively evaluated under challenging scenarios, including parameter uncertainties and load disturbances. Results confirm its ability to maintain stability and performance across a wide range of conditions, outperforming existing methods. This study presents a notable contribution by introducing an innovative control structure that addresses critical challenges in AVR systems, paving the way for more resilient and efficient power system operations. Full article

There are many techniques for the extension and generalization of fractional theories, one of which improves fractional operators by means of their kernels. This paper is devoted to the most general concept of interval-valued functions, studying fractional integral operators for interval-valued functions, along with the multi-variate extension of the Bessel–Maitland function, which acts as kernel. We discuss the behavior of Hermite–Hadamard Fejér (HHF)-type inequalities by using the convex fuzzy interval-valued function (C-FIVF) with generalized fuzzy fractional operators. Also, we obtain some refinements of Hermite–Hadamard(H-H)-type inequalities via convex fuzzy interval-valued functions (C-FIVFs). Our results extend and generalize existing findings from the literature. Full article

Integral inequalities with generalized convexity play a vital role in both theoretical and applied mathematics. The theory of integral inequalities is one of the branches of mathematics that is now developing at the quickest rate due to its wide range of applications. We define a new Hermite–Hadamard inequality for the novel class of coordinated ƛ-pre-invex fuzzy number-valued mappings (C-ƛ-pre-invex FNVMs) and examine the idea of C-ƛ-pre-invex FNVMs in this paper. Furthermore, using C-ƛ-pre-invex FNVMs, we construct several new integral inequalities for fuzzy double Riemann integrals. Several well-known results, as well as recently discovered results, are included in these findings as special circumstances. We think that the findings in this work are new and will help to stimulate more research in this area in the future. Additionally, unique choices lead to new outcomes. Full article

To construct Caputo fractional-order time-delayed cellar neural networks (FOTDCNNs) that characterize real environments, this article introduces partial uncertainties, fuzzy operators, and nonlinear activation functions into the network models. Specifically, both the fuzzy AND operator and the fuzzy OR operator are contemplated in the master–slave systems. In response to the properties of the considered cellar neural networks (NNs), this article designs a new class of mixed control protocols that utilize both the error feedback information of systems and the sampling information of impulse moments to achieve network synchronization tasks. This approach overcomes the interference of time delays and uncertainties on network stability. By integrating the fractional-order comparison principle, fractional-order stability theory, and hybrid control schemes, readily verifiable asymptotic synchronization conditions for the studied fuzzy cellar NNs are established, and the range of system parameters is determined. Unlike previous results, the impulse gain spectrum considered in this study is no longer confined to a local interval (−2, 0) and can be extended to almost the entire real number domain. This spectrum extension relaxes the synchronization conditions, ensuring a broader applicability of the proposed control schemes. Full article

The correct derivation of integral inequalities on fuzzy-number-valued mappings depends on applying fractional calculus to fuzzy number analysis. The purpose of this article is to introduce a new class of convex mappings and generalize various previously published results on the fuzzy number and interval-valued mappings via fuzzy-order relations using fuzzy coordinated ỽ-convexity mappings so that the new version of the well-known Hermite–Hadamard (H-H) inequality can be presented in various variants via the fractional integral operators (Riemann–Liouville). Some new product forms of these inequalities for coordinated ỽ-convex fuzzy-number-valued mappings (coordinated ỽ-convex FNVMs) are also discussed. Additionally, we provide several fascinating non-trivial examples and exceptional cases to show that these results are accurate. Full article