Digital Commons Network^™

Disturbance-Learning Inertia Estimation Using Artificial Neural Networks For Power System Stability, Sospeter Igaanja Gabriel, Francis Mwasilu, Peter Makolo Dr.

Tanzania Journal of Engineering and Technology (TJET)

ABSTRACT

Power systems are progressively shifting towards low inertia as a result of incorporating significant amounts of intermittent and converter-based renewable energy sources, such as wind and solar power, into the current power grid network. This integration poses considerable problems to inertia and frequency control within the network due to a reduction in the proportion of synchronous generators. Furthermore, rapid frequency deviations occur due to the disparity between supply and demand during contingencies, complicating the maintenance of frequency stability within the power system. The disturbance-learning inertia estimation method for power system stability is presented. The simulation analysis is performed using …

Feasibility Of Acetylated Icacina Trichantha Oliv. Tuber Starch As Viscosity And Fluid Loss Control Agents In Water-Based Drilling Mud. A Comparative Assessment With Commercial Starch, Kennedy I. Ogunwa, Theresa O. Uchechukwu, Ozioma Achugasim, Leo C. Osuji, Onyewuchi Akaranta

Tanzania Journal of Science

The need for sustainable, non-food drilling fluid additives has prompted the feasibility assessment of acetylated Icacina trichantha tuber starch as a viscosity modifier and fluid loss control agent in water-based muds. Native starch was acetylated to obtain two derivatives (Degree of Substitution DS = 0.38 and 0.68) and characterized using FTIR, swelling power, and physicochemical analyses. Drilling mud performance was assessed through rheology, gel strength, filtration loss, sorptivity, and model fitting. Acetylation improved swelling and reduced gelatinization temperature. The optimal formulation, Mud-ITO-ACS2 (DS = 0.68), exhibited the most favorable performance, combining low plastic viscosity (6 cP vs 9 cP for …

A Data-Driven Neutrosophic Multi-Criteria Intelligent System For Functional Disability Severity Assessment, Ehab Abdel Aziz Al-Beblawi, Mirna Samy, Abduallah Gamal

Neutrosophic Systems with Applications

Functional disability assessment is a multidimensional problem because individuals may experience different levels of difficulty across daily activities such as walking, standing, dressing, eating, grasping objects, and social participation. This paper proposes NIFDA, a data-driven neutrosophic multi-criteria intelligent system for functional disability severity assessment. The proposed framework represents each functional response through three components: confirmed limitation, indeterminacy, and preserved functional ability. This allows the model to handle valid responses, uncertain information, and missing or non-informative data without forcing them into a single crisp score. To reduce dependence on subjective expert weighting, NIFDA derives criterion weights objectively using a hybrid CRITIC–MEREC …

A Neutrosophic Memory-Integrity Calculus For Contradiction-Preserving Persistent Ai Agents, Rana Muhammad Zulqarnain, Saalam Ali

Neutrosophic Systems with Applications

Persistent AI agents increasingly convert interaction histories into long-lived memory, making memory transformation not retrieval alone—a central reliability problem. NMIC (Neutrosophic Memory-Integrity Calculus) formalizes the integrity of write, merge, consolidation, revision, and retrieval operations over persistent memory. Each proposition is represented through an evidence ledger carrying independent truth, indeterminacy, and falsity degrees together with reliability, provenance, temporal validity, contextual applicability, and inter-evidence dependence. A dependence-normalized hazard aggregation preserves simultaneous support and opposition while making the resulting state invariant to exact evidence duplication. Pure consolidation is governed by five integrity conditions: no support invention, no opposition invention, no manufactured certainty, contradiction …

Neutrosophic Indeterminacy-Transport Kolmogorov-Arnold Networks For Structured Uncertainty, Hafiz Muhammd Bilal, Kiran Naz, Anjum Ijaz

Neutrosophic Systems with Applications

Kolmogorov-Arnold Networks (KANs) replace fixed node activations with learnable univariate edge functions, but standard KAN inference treats two equal-valued features identically even when one is accompanied by an explicit quality warning. NIT-KAN introduces a neutrosophic indeterminacy-transport mechanism for this setting. Each node carries an indeterminacy state in [0, 1]; a monotone gate g(I)=⁽1-I)^α attenuates uncertain evidence on the predictive path, while a sensitivity-weighted transport rule carries indeterminacy through the underlying KAN computation. A terminal audit maps signed evidence to truth-support, falsity-support, and conflict-augmented indeterminacy without interpreting these quantities as class probabilities. We …

Neutrokoopman: Channel-Preserving Koopman Spectral Analysis Of Nonlinear Dynamics With Truth, Indeterminacy, And Falsity Evidence, Ahmed Samy, Mohamed M. Abdelhafeez, K Venkatachalam, Mohamed Abouhawwash

Neutrosophic Systems with Applications

Modern dynamical systems increasingly operate with evidence that is not merely noisy but incomplete, contradictory, or only partially trustworthy. Conventional Koopman methods represent nonlinear dynamics through linear evolution of observables, while robust and adaptive variants address parameter and model uncertainty. They do not, however, preserve the semantic distinction between support, indeterminacy, and counter-support when those conditions are compressed into a single uncertainty variable. This paper develops NeutroKoopman, a channel-preserving Koopman framework in which the physical state is augmented by a single-valued neutrosophic evidence state ν_t = ( T_t,I_t,F_t ). Deterministic and Markovian formulations are …

Thermo-Entropic Analysis Of Unsteady Mhd Nanofluid Couette Flow: A Classical Spectral Approach With An Exploratory Quantum Linear-Solver Application, Serai Israel Mosala, Oluwole Daniel Makinde, Azwinndini Muronga

Mathematical Modelling and Numerical Simulation with Applications

This study presents a thermo-entropic analysis of unsteady MHD nanofluid Couette flow, combining a classical bivariate spectral quasilinearisation (BI-SQLM) and Crank--Nicolson solution with an exploratory application of quantum linear solvers. An incompressible, electrically conducting nanofluid flows between parallel plates under partial slip and convective heat exchange; the discretised linear systems are additionally solved via the Harrow--Hassidim--Lloyd (HHL) algorithm and the Variational Quantum Linear Solver (VQLS). Results are presented for Pure Water, Cu-Water, and Al₂O₃-Water across seven parameter variations. The Hartmann number dominates velocity suppression and entropy amplification, while velocity slip reduces upper-wall entropy generation more than …

Effects Of Co-Rotating Cylinders And Boundary Conditions On Heat And Mass Transfer In A Trapezoidal Enclosure, Olalekan Adebayo Olayemi, Olalekan Tajudeen Popoola, Leke Thaddeus Oladimeji, Isaac Kayode Adegun, Taofiq Omoniyi Amoloye, Oluwatayo Babatope Ojo, Benjamin Ohida Daniel, Michael Olabode Ibiwoye

Mathematical Modelling and Numerical Simulation with Applications

This study investigates the effects of rotational speed $(\Omega)$, Richardson number $\left(Ri\right)$, and Lewis number $\left(Le\right)$ on heat and mass transfer (HMT) around two co-rotating cylinders symmetrically situated in a trapezoidal enclosure. In the double-diffusive mechanism, the bottom wall of the trapezium is at a high temperature $\left(T_h\right)$ and concentration $\left(C_h\right)$, while the top wall is at a low temperature $\left(T_c\right)$ and concentration $\left(C_c\right)$. The side walls are perfectly insulated with zero mass flux, while the buoyancy ratio $\left(Br\right)$ is fixed at 1.0.  Two thermal-mass boundary scenarios are considered. Scenario 1: Cylinders are thermally insulated with zero mass flux, and …

Decision Support System For The Selection Of Thumbprint Recognition Algorithms In Biometric Security Systems, Toqeer Jameel, Muhammad Riaz

Neutrosophic Systems with Applications

This study investigates fingerprint recognition in immigration operations, emphasizing the role of biometric verification in enhancing security, fairness, and operational efficiency in international mobility. To address the uncertainty, vagueness, and imprecision inherent in fingerprint identification, a novel decision-making framework is proposed by integrating interval-valued picture fuzzy (IVPF) information. Fairly aggregation operators are introduced to combine decision makers' evaluations, while extracted fingerprint features are modeled using positive, neutral, and negative membership degrees within the IVPF environment. Objective criterion weights are determined using the criteria importance through intercriteria correlation (CRITIC) method, and individual ranking is performed via the alternative ranking order method …

Fast Sparse Image Reconstruction Models In Through-The-Wall Radars: A Review, Aude Kileo, Hashimu U. Iddi, Abdi Abdalla

Tanzania Journal of Science

Through-the-Wall Radar Imaging (TWRI) is a modern technology that uses electromagnetic waves to detect objects behind walls, with key applications in surveillance, rescue operations, and reconnaissance. Achieving high resolution in both down-range and cross-range requires ultra-wideband signals and long apertures, resulting in large data volumes, increased acquisition time, and high memory demands. TWRI employs Compressive Sensing (CS) to reduce computational time, which has proved its significance in many recent TWRI applications. However, in CS, image reconstruction approaches shift the computational burden from the sensing stage to the recovery stage, which prolongs the reconstruction times, making it unsuitable in time-sensitive applications. …

Effect Of Acid Pretreatment On The Adsorption Of Phenol Red By Cellulose Isolated From Sandbox (Hura Crepitans L.): Kinetics, Isotherms And Thermodynamics, Muibat O. Alabi-Abass, Najeem A. Adesola Babarinde, Adeola A. Ibikunle, Nurudeen O. Sanyaolu

Tanzania Journal of Science

The removal of synthetic dyes from wastewater remains an urgent environmental concern due to their persistence and toxicity. This study evaluates cellulose from Hura crepitans (HC) and its acid-modified form (AHC) for the adsorption of phenol red (PR) dye from aqueous solutions. The adsorbents were characterized using FTIR to identify functional groups responsible for dye binding, XRD to determine the crystalline structure, SEM to examine surface morphology, and BET/BJH analyses to measure surface area and pore distribution. Acid modification enhanced surface roughness, porosity, and the abundance of active functional sites, leading to improved adsorption efficiency. Optimal conditions were pH 9.0 …

Efficient Acidic H2O2 Electrosynthesis Over Co Atoms Anchored On Nitrogen-Doped Hierarchical Porous Carbon, Xiao Huang, Zi-Hao Zhan, Hao-Xu Niu, Guan-Yu Luo, Jin-Tao Huang, Bo-Xuan Jin, De-Li Wang

Journal of Electrochemistry

The two-electron oxygen reduction reaction (2e⁻ ORR) presents a promising route for the on-site production of hydrogen peroxide (H₂O₂), offering a green alternative to energy-consuming anthraquinone process. However, the high selectivity toward the competing 4e⁻ ORR over the desired 2e⁻ pathway leads to low Faradaic efficiency for H₂O₂, posing a critical challenge in catalyst design. In this work, a nitrogen-doped hollow hierarchical porous carbon with anchored Co atoms (CoN/HPC) was constructed for high-performance H₂O₂ production. The as-prepared Co-N/HPC catalyst showed excellent 2e⁻ ORR performance, achieving …

Engineering Small-Sized Cations With Strong Solvation For High-Voltage Supercapacitors, Tong Huo, Pan Liu, Hao Chen, Peng Zhang, Zhen-Lei Chen, Guo-Fu Sun, Zhi-Qiang Shi, Jing Wang

Journal of Electrochemistry

Supercapacitors (SCs) have attracted much attention in the field of energy storage due to their high power density and long cycle life. However, their relatively low energy density limits their application in a wider range of fields. Methods to increase energy density include enhancing specific capacitance and extending operating voltage window. Herein, we report a novel electrolyte salt, tetramethylammonium bis(fluorosulfonyl)imide (TMA-FSI), the resulting electrolyte formed with propylene carbonate (PC), designated as TMF, exhibited a wide electrochemical stability window (ESW) of 5.09 V. On the one hand, we found that the small size of TMA⁺ enables it to enter the …

Dense Platinum Nanoparticles Confined In Mn-N-C Nanocages For Robust Heavy-Duty Pemfcs, Lei Zhao, Zhen-Min Cao, Jia-Yu Zuo, Ming-Liang Yang, Hong-Yan Qiao, Jun-Song Chen, Rui Wu

Journal of Electrochemistry

High-loading Pt cathodes are essential for heavy-duty proton exchange membrane fuel cells but suffer from a critical tradeoff between ionomer sulfonate poisoning and nanoparticle instability. Herein, we report a spatial confinement strategy to encapsulate dense Pt nanoparticles (~51.8 wt%) within Mn/N-co-doped mesoporous carbon nanocages (denoted as Pt-MnNC). This architecture excludes bulky ionomers to create an ionomer-shielded environment against sulfonate poisoning, while Mn-N_x-mediated strong metal-support interactions anchor the Pt nanoparticles to prevent agglomeration and further boost durability. In the 5 × 5 cm² membrane electrode assembly tests, the Pt-MnNC catalyst delivers an exceptional power density of 1.26 W·cm …

Chacahoula 2026, Volume 93, Pawan Khatri, Alayna Pellegrin

Chacahoula

The 2026 issue of Chacahoula, There's No Place Like Home, emphasizes the way in which our university becomes a home to students.  Featuring prize-winning poetry from Allyson Gee, Nick Fredrick, Amaris Kendall and Anmol Subedi, the book includes December 2025 and May 2026 graduating classes, as well as stunning photography of  campus life on the banks of Bayou Desiard. With features covering students, faculty, and staff, this year's time capsule preserves an academic year in the life of the University of Louisiana at Monroe. This edition includes a video message from President Carrie Castille. Download the PDF to your device …

Increasing The Density Value Of Composite Propellant Ap/Htpb/Al For Rocket Performance Using The Sphere Packing Model, Muhammad Iqbal Alfikri, Bayu Prianto, Ariadne Lakshmidevi Juwono

Makara Journal of Science

This research employs the sphere packing volume (SPV) model to optimize the density of composite propellants thereby enhancing rocket performance. We systematically varied the diameters of spherical ammonium perchlorate (AP) particles (50 and 400 µm) and aluminum powder (Al) particles (10 and 30 µm). These particles were incorporated at an ideal AP:Al ratio of 4:1 into a matrix of hydroxyl-terminated polybutadiene and toluene diisocyanate. By adjusting the AP ratio for each Al particle size and considering theoretical and tap densities, we precisely calculated the SPV value for each composition. The composition with the highest SPV value yielded remarkable results. This …

Geotechnical Enhancement Of Noncohesive Coal Mine Overburden Using Microbially Induced Calcite Precipitation And Fly Ash Stabilization For Sustainable Pavement Applications, Atul Gautam, Laxmikant Yadu

Journal of Sustainable Mining

The majority of the waste generated from opencast mining, termed as overburden (OB), is dumped adjacent to the mine boundary and poses long-term stability issues. Various studies have been carried out to assess the risk of dump slope failure, and treatment strategies have been accordingly recommended to strengthen this OB dump. However, scant works are reported to sustainably utilize this OB for construction works. This study aims to quantify the performance of stabilized noncohesive coal mine OB collected from Gondegaon opencast mine (OCM) in India. Stabilization was achieved using two techniques, namely microbially induced calcite precipitation (MICP) through Sporosarcina pasteurii …

Reducing Drag Of Vehicles With Half-Stepped Cylinder Surfaces Part I: Numerical Optimization, Ryan Moffit, Hamid Rahai, Komal Gada

Mineta Transportation Institute

Aerodynamic drag is a major source of energy loss for vehicles, increasing fuel consumption, reducing the driving range of electric vehicles, and contributing to greenhouse gas emissions. For a vehicle, the major components of its drag are pressure and viscous drag. Engineers have developed a variety of strategies to reduce drag, including streamlined vehicle designs, surface modifications, and devices that alter airflow around the vehicle. This research focuses on reducing the viscous drag. Specifically, this study investigates whether microscopic surface features called half-stepped cylinder arrays can reduce skin friction by altering the turbulent boundary layer and promoting smoother airflow near …

Modeling Preannouncement And Launch Timing Decisions For Product Line Extension Under Innovation Uncertainty And Resource-Sharing Dual-Sourcing Supply Chain, Adewole Adegbola, Venkat Allada

Engineering Management and Systems Engineering Faculty Research & Creative Works

This research seeks to develop a system dynamics (SD) model to address challenges associated with the product line extension (PLE) problem. In this work, we specifically consider the "Two-Generation Product Line Extension (TGPLE) Problem", where a firm introduces a product variant in the market and plans to extend the launch to include a newer generation product in a resource-sharing dual-sourcing (RSDS) environment. We begin by identifying the key factors that influence product line extension, and we developed a TGPLE-RSDS construct based on three (3) inter-related systems: Market System, Production System and Supply Chain System. The proposed TGPLE-RSDS construct also illustrates …

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

Integrated Fire Protection Design In Pharmaceutical Manufacturing Facilities: An Applied Case Study, Eman Osman Mahmoud Osman, Fouad Abdel-Mawgoud Abdel-Halim Qasim, Alaa El-Din El-Sayed Farid Hassan

HBRC Journal

The architectural design of pharmaceutical manufacturing facilities presents a complex engineering challenge, requiring simultaneous compliance with Good Manufacturing Practice (GMP) containment standards and life safety egress codes. This research presents an applied methodology for integrated fire protection design in sterile pharmaceutical environments, demonstrated through a case study of a manufacturing complex comprising cleanroom production zones and high-bay storage warehouses with heights reaching 14.8 m. The study reconciles facility classification under the Egyptian Fire Code and NFPA standards by defining Occupancy Hazard Groups W-1 and W-2 (high- and moderate-hazard storage), while utilizing regulatory Code Tables C-1 and C-2 to calculate occupant …

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

Design Of Permanent Magnet Synchronous Motor For Railways Traction Application, Anna S. Mwang'onda, Jackson J. Justo, Francis Mwasilu

Tanzania Journal of Engineering and Technology (TJET)

This paper presents the design of surface mounted permanent magnet synchronous motor (SMPMSM) intended for traction applications in standard gauge railway (SGR) for passengers. The proposed motor is rated 350 kW with a base speed of 3000 rpm. Moreover, speed control strategy with stability analysis is incorporated in the design to validate the performance of the SPMSM. The simulation results achieved a torque of approximately 1115 Nm at a terminal voltage of 800 V. The flux linkage of 150 mVs which ensures effective electromagnetic pertinence while maintaining safe magnetic loading because the flux density is less than 1.5 T. The …

Comparison Of Gravity Separation And Flotation For Pyrite Recovery From Tailings, Gülay Bulut, Gönül Göksu Gökçe, Dilruba Karamanlı, Oğuzhan Mert Gürkan, Ergin Sarp Zenzirci, Binnur Kırım, Alim Gül

Journal of Sustainable Mining

Tailings generated during the production of lead, zinc, and copper concentrates contain significant amounts of sulfide minerals, particularly pyrite. Pyrite, which remains in tailings after the recovery of other metals, is one of the main contributors to acid mine drainage (AMD). Therefore, recovering pyrite from tailings is environmentally and economically an important issue. In this study, pyrite recovery from tailings was investigated using flotation and gravity separation methods. Mineralogical characterization and liberation analyses were conducted by Mineral Liberation Analysis (MLA), indicating that pyrite is the major sulfide mineral with over 76% liberation degree even in the coarsest fractions. Flotation tests …

Challenges, Trends, And The Role Of Lstm In Ai-Based Predictive Maintenance Of Electrolyzers For Solar Hydrogen Systems: A Review, Yani Koerniawan Kuatno, Muhamad Zahim Sujod

Turkish Journal of Electrical Engineering and Computer Sciences

The transition toward low-carbon energy systems has increased interest in hydrogen as a clean energy carrier, with solar-driven water electrolysis emerging as a promising technology due to its high efficiency and compatibility with renewable energy sources. However, dynamic operating conditions and intermittent renewable input accelerate electrolyzer degradation, reducing reliability and system lifespan. Predictive maintenance (PdM), supported by artificial intelligence (AI), offers a data-driven approach to anticipate failures and improve operational durability. This review systematically investigates AI-based PdM approaches for electrolyzers, with an emphasis on long short-term memory (LSTM) networks and Internet of things (IoT) integration. Following PRISMA 2020 guidelines, 35 …

Dynamic Focal Loss Adjustment For Railway Defect Detection, Mehmet Koç, Ridvan Özdemi̇r, Ömer Gerek

Turkish Journal of Electrical Engineering and Computer Sciences

Railway infrastructure is critical to the safe and efficient operation of transportation systems, and the early detection of defects is essential for preventing catastrophic failures. Automated defect detection methods are therefore crucial for maintaining continuous safety while reducing maintenance costs. Although Focal Loss is widely used in object detection under class-imbalanced conditions, its fixed α parameter may limit its effectiveness in detecting rare defects. In this study, we propose an adaptive α-tuned Focal Loss approach that dynamically adjusts class weights based on average precision (AP) values. By iteratively optimizing α without relying on gradient-based optimization, the proposed method improves the …

Relative Rate Observer-Based Online Tuning Mechanism For Single-Input Interval Type-2 Fuzzy Pid Controllers, Oqba Aldreiei, Cenk Ulu, Mert Can Kurucu, Müjde Güzelkaya

Turkish Journal of Electrical Engineering and Computer Sciences

The characteristics of the footprint of uncertainty (FOU) in interval type-2 membership functions (IT2-MFs) are crucial to the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). However, existing IT2-FC design approaches mostly use fixed FOU structures. This study proposes an online membership function (MF) adjustment mechanism for a single-input interval type-2 fuzzy PID controller (SIT2-FPID)  that adjusts the FOU of the antecedent MFs and weights of the consequent MFs, respectively, to achieve high performance and robustness. The proposed online adjustment mechanism consists of a relative rate observer (RRO), a two-input rule-base adjustment system, and a first-order smoothing filter. The …