Digital Commons Network^™

The All Of Us Evenings With Genetics Research Program, Stacy M Lloyd, Jasmine Baker, Carolina Jorgez, Julie Coleman, Laterrica Williams, April Adams, Ashley Butler, Elizabeth Atkinson, Shamika Ketkar, Steven Scherer, Kim Worley, Susan Fernbach, Keasia Daniels, Latanya Hammonds-Odie, Laura Rosales, Brendan Lee, Debra Murray

Faculty, Staff and Students Publications

The All of Us Evenings with Genetics (All of Us EwG) Research Program, funded in part by the National Institute of Health’s All of Us Research Program, engages researchers to utilize the All of Us Researcher Workbench to advance precision medicine. The All of Us EwG program has two core aims: (1) address barriers to research productivity for minorities, disadvantaged, and persons with disabilities aspiring to enter STEM fields; and (2) promote the use of the All of Us Researcher Workbench to develop research focused on health equity. To achieve these goals, the program implements three key activities: (1) …

Experimental Investigation Into The Effects Of Air Staging On Fouling In A Fixed-Bed Biomass Combustor, Akram Elsebaie

Theses: Doctorates and Masters

Biomass offers significant potential as a sustainable and renewable substitute for fossil fuels in energy generation, playing a critical role in the reduction of global warming. However, despite its environmental benefits, biomass combustion faces persistent operational challenges, particularly fouling, which reduces efficiency and increases maintenance costs. Although fouling mechanisms have been studied in various combustion systems, there is limited research which addresses its formation and behaviour in fixed-bed biomass combustors under different air staging conditions. This research investigates fouling behaviour in a counter-current combustor, focusing on the influence of design (secondary air location), operational (air staging flowrates between the primary …

Tools Used To Measure Fear Avoidance Behaviour In The Management Of Mild Traumatic Brain Injury (Concussion), Liam Sherwood

Theses: Doctorates and Masters

Concussion can present challenges with respect to diagnosis and management. Fear avoidance behaviour and beliefs may be perpetuating factors in the clinical course of concussion. Understanding which patients may adopt such behaviours and beliefs can offer important information to allow tailoring of clinical management and implementing strategies to minimise the risk of progression to persisting post-concussion symptoms.

In this thesis, I:

-       systematically reviewed the current measurement tools used to assess fear avoidance behaviours and beliefs associated with concussion;

-       explored the experience of clinicians, researchers and people with lived experience of concussion in relation to fear avoidance behaviours;

- …

Synergistic Removal Of Microplastic Fibres: Integrating Chitosan Coagulation In Hybrid Water Pre-Treatment Systems, Jayasuriya Arachchilage Nimesha Thathsarani

Theses: Doctorates and Masters

Microplastic fibres (MPFs) are the most prevalent form of microplastics detected in the influents of water and wastewater treatment plants. Owing to their elongated shape and low density, MPFs frequently evade removal even during tertiary processes such as membrane filtration. Enhancing pretreatment, particularly through cost-effective coagulation-flocculation can reduce the treatment burden on downstream systems. However, conventional inorganic coagulants often necessitate high dosages and can leave residual ions, posing environmental concerns.

As a sustainable alternative, the use of environmentally friendly coagulants such as Chitosan has gained attention for MPF removal. While Chitosan exhibits favourable qualities, its practical application is hindered by …

Integrated Incubators: A Novel Framework For Technology-Based Creative Arts Education, Brad Phelps Nisbet

Theses: Doctorates and Masters

This thesis investigates the development and impact of an educational framework designed to bridge the gap between academia and industry within the media and entertainment sector. It addresses the prevalent "skills mismatch" where graduates often lack the specific technical competencies, foundational knowledge, practical experience, or soft skills required for entry-level positions in a rapidly evolving landscape influenced by advancements like virtual production, real-time VFX, immersive media, and Al. Grounded in both theoretical and practical insights, the framework aims to foster student creativity, technical proficiency, industry readiness, transferable skills, and adaptability, prioritising a transformative, student-centric learning experience.

The research employed a …

Getting Ready Right: The Impact And Influence Of The University Appointed Supervisor, Madlen Griffiths

Theses: Doctorates and Masters

Initial Teacher Education professional experience is a valued component of teacher education courses, where preservice teachers can practise applying what they have learned at university in classroom settings. Key to these experiences are supervisors, who are employed by the university and support these aspiring teachers during their professional experiences. This supervisory role is complex, underrated, under-theorised and under researched. Consequently, this research sought to address this shortfall through an investigation into the perspectives of two of the major stakeholders in these experiences: the preservice teachers and the supervisors.

Adopting a social constructivist methodology, this research examined the stakeholders’ perceptions of …

Development Of Virtual Reality Learning Environments In Science To Engage Secondary Students In Hazardous Activities, Luke Spartalis

Theses: Doctorates and Masters

This research examines the need for change in including hazardous activities in education. As different learning technologies develop, platforms that retain authentic outcomes via virtual reality are needed. The main objective of this research is to examine the inclusion of Virtual Reality Learning Environments (VRLEs) to determine their value in areas that include hazardous conditions. The research considered the ways that VR tools could be optimised to support these activities, as well as considering the challenges of VRLE implementation. The study examined whether VRLE’s allowed for authentic experiences to sufficiently drive an acceptance of VR to complement existing teaching and …

The Development Of A Midwifery Specific Tool To Identify The Barriers And Enablers To Change Within The Midwifery Setting, Sara Davis

Theses: Doctorates and Masters

Despite being well-positioned to identify service gaps and propose improvements, many midwives lack the knowledge, skills, confidence, or tools to effect practice change. Implementation science (IS) offers a structured approach to moving evidence-based innovations into practice; however, existing IS theories, models, and frameworks do not reflect the unique context of midwifery practice. The first step in the intentional implementation of an evidence-based practice innovation is to conduct a context assessment to identify factors that may facilitate or impede its uptake. There are numerous evidence-based checklists for this, but none capture all the influences on midwifery practice environments.

In this study, …

The Development And Pilot Evaluation Of An Out-Of-Hospital Immersive Birthing Simulation Training Application For Paramedics And Ambulance Officers Using Virtual-Reality Technology, Michella Hill

Theses: Doctorates and Masters

Out-of-hospital births (OOHBs) are births which occur outside a hospital setting. This includes planned home births attended by midwives, or unplanned OOHBs where the intention was to birth in a hospital or maternity centre, however, occurs in an unintended location. Prehospital clinicians, inclusive of registered paramedics and volunteer ambulance officers (VAOs) infrequently attend OOHBs, as most births occur in maternity wards as anticipated. OOHBs comprises < 1% of emergency ambulance requests and requires specialised skills for prehospital clinicians to competently manage birthing and potential complications. As exposure to birthing is so rare, these specialised skills and knowledge are underutilised and subject to rapid decay. Compounding the problem, obstetrics is infrequently taught across undergraduate education, in-service training, or within individual clinician’s continuing professional development. Placements shadowing midwives are scarce due to nursing and medical students receiving priority. Previous research regarding OOHBs is limited, nonetheless suggests prehospital clinicians are underprepared to manage an OOHB, which has serious patient safety implications. The present research sought to explore prehospital clinicians’ preparedness and confidence in managing OOHBs. It also investigates whether a digital training solution such as virtual reality (VR) simulation may reduce the theory-practice gap and assist in competency retention.

Publication one provides a comprehensive methodology outline which is applied to Study One; a scoping review of prehospital clinician and birth parent experiences with OOHBs. Risk factors, complications and interventions provided by prehospital clinicians, as …

Enhancing Deep Reinforcement Learning With Expert Demonstrations For Mobile Robot Navigation In Unstructured Off-Road Environments, Dulitha Dabare

Theses: Doctorates and Masters

Advancements in the field of Deep Learning has ushered in a boom in autonomous navigation research. Most of the work being conducted in this space, however, has focused on on-road urban navigation scenarios, with unstructured outdoor terrain navigation receiving much more limited attention. Given the wide range of applications that exist for legged and wheeled ground robots in off-road environments in areas such as agriculture, mining and disaster recovery, there is a growing need for research work to improve the navigational capabilities of mobile robots deployed in these challenging environments.

A promising candidate for application to these navigation challenges in …

Understanding Climate Change And Wellbeing Through Bhutan's Pursuit Of Gross National Happiness, Tashi Dorji

Theses: Doctorates and Masters

Climate change has evolved into one of the biggest global challenges of the twenty first century reshaping ecological systems and social conditions with wide-ranging implications for human wellbeing. Bhutan offers a distinct context to explore interactions between climate change and wellbeing as it remains guided by its wellbeing-centred development philosophy of Gross National Happiness (GNH). At the same time, it remains highly vulnerable to climate change despite being the only carbon negative country in the world. Nonetheless, the intersections between climate change and GNH have not been systematically explored.

This research aims to explore the relationship between climate change and …

Emotional Coding In Wolfgang Amadeus Mozart’S Music: The Influence Of Text-Driven Musical Patterns, Antonio Toppi

Theses: Doctorates and Masters

This thesis investigates the phenomenon of emotional coding in the music of Wolfgang Amadeus Mozart (1756–1791), with particular attention to the ways in which text-driven musical patterns influence both vocal and instrumental works. The study draws on 18th-century theories of key characteristics, musical-rhetorical figures, and topoi, compositional devices that carried emotional implications for contemporary listeners. These codes are first examined in two of Mozart’s operas, Le Nozze di Figaro (KV492) and Don Giovanni (KV527), where the setting of text to music provides a framework for associating compositional choices with emotion. Findings from the analyses are then applied to three contemporaneous …

Multimodal Scene Understanding For Mobile Robot Navigation In Unstructured Outdoor Environments, Liyana Mudiyanselage Nipuna Dinendra Wijayathunga

Theses: Doctorates and Masters

The application of autonomous field robots in agriculture, mining, transport and logistics, disaster management, and exploration has increased with advances in sensing, perception, and autonomous navigation technologies. Western Australian off-road environments present challenges for autonomous ground robots because they contain unstructured ter rain, variations in appearance and geometry, changing illumination, and visually similar regions with different traversal characteristics. Reliable operation therefore requires scene understanding methods capable of representing the environment according to its navigability for robot traversal.

This PhD research investigates multimodal scene understanding for autonomous ground robot navigation in unstructured off-road environments. The research focuses on the development of …

Multi-Period Coordinated Planning Of Xfcs In Coupled Tn-Pdn Networks: Integrating Demand Charge Reduction And Pre-Existing Infrastructure, Waqas Ur Rehman, Siyuan Wang, Liheng Lv, Jonathan W. Kimball, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate charging infrastructure support. This paper aims to address the latter bottleneck and proposes a strategic multi-period coordinated planning model to optimally site and size battery energy storage system (BESS) assisted extreme fast charging stations in a highway transportation network and solar systems in a power distribution network. The proposed approach accounts for pre-existing charging stations, the increasing EV penetration levels, decreasing technology costs, and technological advancements in the future and …

Large Language Models For Neurology: A Mini Review, Donald C. Wunsch, Daniel B. Hier

Electrical and Computer Engineering Faculty Research & Creative Works

Large language models have the potential to transform neurology by augmenting diagnostic reasoning, streamlining documentation, and improving workflow efficiency. This Mini Review surveys emerging applications of large language models in Alzheimer's disease, Parkinson's disease, multiple sclerosis, and epilepsy, with emphasis on ambient documentation, multimodal data integration, and clinical decision support. Key barriers to adoption include bias, privacy, reliability, and regulatory alignment. Looking ahead, neurology-focused language models may develop greater fluency in biomedical ontologies and FHIR standards, improving data interoperability and supporting more seamless collaboration between clinicians and AI systems. Two future developments have the potential to be particularly impactful: (1) …

Mitigating Hysteresis In Metal-Coated Fibers Via Optimized Thermal Treatment For Advanced Distributed High-Temperature Sensing Applications, Koustav Dey, Rony Kumer Saha, Bohong Zhang, S. Narasimman, Farhan Mumtaz, Jeffrey D. Smith, Rex E. Gerald, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Metal-coated optical fibers are widely employed in sensing applications owing to their superior mechanical strength and corrosion resistance. However, their calibration at elevated temperatures is hindered by hysteresis, manifested as discrepancies between heating and cooling cycles, primarily caused by residual strain from mismatched thermal expansion coefficients (TECs) between the metal coating and silica cladding. This research introduces an optimal heat treatment procedure aimed at minimizing the impact of the mismatch in TECs between the cladding and the coating materials that causes the residual strain in gold (Au) and copper (Cu) coated fibers for achieving reliable distributed high temperature sensing up …

Evaluation Of Multiple Generative Large Language Models On Neurology Board-Style Questions, Mohammad Almomani, Vijaya Valaparla, James Weatherhead, Xiang Fang, Alok Dabi, Chih Ying Li, Peter Mccaffrey, Dan Hier, Jorge Mario Rodríguez-Fernández

Electrical and Computer Engineering Faculty Research & Creative Works

Objective: To compare the performance of eight large language models (LLMs) with neurology residents on board-style multiple-choice questions across seven subspecialties and two cognitive levels. Methods: In a cross-sectional benchmarking study, we evaluated Bard, Claude, Gemini v1, Gemini 2.5, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, and ChatGPT-5 using 107 text-only items spanning movement disorders, vascular neurology, neuroanatomy, neuroimmunology, epilepsy, neuromuscular disease, and neuro-infectious disease. Items were labeled as lower- or higher-order per Bloom's taxonomy by two neurologists. Models answered each item in a fresh session and reported confidence and Bloom classification. Residents completed the same set under exam-like conditions. Outcomes included overall and …

Multi-Parameter Optimization And Adaptive Temperature Compensation For Fbg Strain Sensors In Wide-Temperature-Range Aerospace Applications, Ruling Zhou, Jiacheng Yu, Yutang Dai, Jianguan Tang, Minghong Yang, Farhan Mumtaz

Electrical and Computer Engineering Faculty Research & Creative Works

Accurate strain measurement in cryogenic fuel pipelines is crucial for ensuring the structural integrity and reliability of rocket engine systems operating under extreme thermal conditions. Fiber Bragg grating (FBG) sensors show significant potential for such applications; however, their inherent temperature-strain cross-sensitivity limits performance over wide temperature ranges. This research presents an enhanced compensation strategy combining multi-parameter optimization with temperature-zone-specific adaptation to improve the accuracy and stability of FBG-based strain sensing in harsh aerospace environments. Four special steel substrate materials including S03, S06, S07, and 1Cr18Ni9Ti, were evaluated using strain transfer theory and thermo-mechanical coupling simulations. Genetic algorithms optimized key design …

Safe Optimal Control Framework For Cooperative Manipulation Of Objects In Human–Robot Teams, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article introduces a distributed deep neural network (NN)-based adaptive control framework for cooperative object manipulation in human–robot teams with unknown agent dynamics by using three distinct multilayer NN observers (MNNOs). The first observer, termed the reference point estimator, enables each robotic agent to estimate the object's reference center using consensus-based learning, even without direct access to global reference trajectories. The second observer, referred to as the human force-to-trajectory estimator, uses human-applied forces to infer the intended position, velocity, and acceleration of the object, enabling real-time estimation of human intent. Together, these two observers allow distributed estimation of human-intended motion. …

High-Resolution, Fast-Response Optical Fiber Temperature Sensor With A Large Measurement Range Based On Fiber-Tip Alumina Fabry-Pérot Interferometer, Ruimin Jie, Chen Zhu, Robert Abbott, Michael Davis, Xiong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We present an alumina-tip optical fiber Fabry-Pérot interferometric temperature sensor exhibiting high-temperature performance, rapid response, and high resolution. Fabricated by fusion splicing an alumina micro disk directly to a single-mode fiber, the sensor achieves robust, stable operation without complex fabrication processes or adhesives. Experimental evaluation confirms a measurement range extending to 1000°C, with sensitivity of 28.66 pm/°C, a resolution of 0.042°C, and a rapid response time of approximately 13 ms. Compared to state-of-the-art optical fiber FPI sensors, our alumina-tip sensor offers superior overall performance, effectively addressing critical demands for high-resolution, fast-response temperature measurement in extreme environments including aerospace, structural monitoring, …

Waveguide-Assisted Single-Mode Fiber Bragg Gratings In A Highly Multimode Coreless Fiber Via Femtosecond Laser Inscription For Extreme Temperature Quasi-Distributed Thermal Sensing, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This research reports a potential quasi-distributed thermal mapping optical sensing system for extreme temperatures, leveraging femtosecond (fs) laser inscribed single-mode fiber Bragg gratings (FBGs) and a waveguide within coreless, highly multimode optical fiber, resulting in a single-mode structure. Unlike doped single-mode fibers, coreless fibers composed of silica rods prevent issues associated with dopant migration and ensure data accuracy. The strategic placement of point-by-point FBGs in a cascaded formation on the fs-laser inscribed waveguide facilitates localized multipoint sensing. The long-term stability of the proposed waveguide-assisted FBG system was assessed over 24 hours at elevated temperatures (1000°C), showing no hysteresis during heating …

Embeddable Optical Fiber Sensor For Simultaneous Strain And Temperature Monitoring, Amardeep Kaur, Sudharshan Anandan, Steve Eugene Watkins, Yinan Zhang, Kumbla Chandrashekhara, Hai Xiao

Electrical and Computer Engineering Faculty Research & Creative Works

We present an embeddable hybrid optical fiber sensor based on a cascaded extrinsic Fabry–Pérot interferometer (EFPI) and intrinsic Fabry–Pérot interferometer (IFPI) for simultaneous strain and temperature monitoring in high-performance composite materials. The sensor is fabricated using femtosecond laser micromachining and is embedded within bismaleimide composite laminates manufactured via an out-of-autoclave process. Experimental results demonstrate linear and decoupled responses to strain and temperature, with the EFPI showing minimal temperature sensitivity (1.7 pm/°C) and the IFPI exhibiting high temperature sensitivity (16.1 pm/°C). Strain sensitivities for both components were consistent at 0.6pm/με in embedded conditions. The sensor maintained structural integrity and stable spectral …

Performance Analysis And Optimization Of Fructose Memristor-Based Neuromorphic Systems, Harshvardhan Uppaluru, Shakil Mahmud Jiban, Shah Zayed Riam, Feng Zhao, Jinhui Wang

Electrical and Computer Engineering Faculty Research & Creative Works

Natural organic memristors have demonstrated promising synaptic behavior, positioning them as strong candidates for synaptic devices in neuromorphic systems. This paper presents the fabrication and evaluation of a neuromorphic system based on natural organic 16-level and 32-level fructose memristors. First, the manufacturing process of fructose memristors is described in detail. Second, the nonlinear property associated with fructose memristors is investigated, and an optimization method is applied to address the nonlinear effects. The performance of the fructose memristor-based neuromorphic system on MNIST with a multi-layer perceptron and on CIFAR-10 with VGG-8 is evaluated and reported under various conditions -with/without nonlinearity optimization …

Integrating Optical And Radiofrequency Interferometry For Enhanced Phase Sensing, Ruimin Jie, Zhaopeng Zhang, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Interferometry is a crucial investigative technique used across diverse fields to achieve high-precision measurements. It works by analyzing the phase difference between two interfering waves, which results from variations in optical path lengths within an interferometer. We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer, importantly, with the added advantage of a controllable enhancement factor. This approach is achieved through a two-step process: first, the optical phase difference is encoded into a sub-GHz radiofrequency (RF) signal using microwave-photonic manipulation; then, RF interferometry-assisted phase amplification is implemented at the destructive interference point. In …

Online Lifelong Optimal Adaptive Control Of Partially Uncertain Strict Feedback Discrete-Time Systems With Application To Quadrotor Uavs, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal adaptive tracking control of partially uncertain strict feedback discrete-time (DT) systems with application to quadrotor uncrewed aerial vehicles (UAVs). First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of the tracking error dynamics. The optimal adaptive tracking control problem is solved using an augmented system approach, where a horizon of future bounded reference trajectory points is used in the augmented state, when compared to using a single point. It is assumed that the internal dynamics of the strict feedback system are unknown, but the …

Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems, Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In sensor-driven dynamic systems, missing data can severely degrade parameter estimation accuracy. This article investigates the impact of missing data on phase estimation in a mass-spring-damper system using an information-theoretic framework based on the Cramér-Rao Lower Bound (CRLB). Closed-form CRLB expressions are derived for four scenarios: complete data, missing completely at random (MCAR) deletion, MCAR-based imputation, and missing at random (MAR) missingness via a selection-weighted formulation. These bounds are used as theoretical benchmarks to evaluate classical imputation methods (last observation carried forward (LOCF), linear interpolation) and advanced approaches (Kalman filtering, Rauch-Tung-Striebel (RTS) smoothing, Bayesian inference, and transformer-based imputation) through Monte …

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

Resolving Stiffness Trade-Offs In Simultaneous Pressure And Vibration Sensing Using A Corrugated-Tube Fiber-Optic Sensor, Yizheng Chen, Yan Tang, Jie Huang, Qi Zhang, Biyao Shi, Zewei Wu

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes and experimentally demonstrates a corrugated-tube-based fiber-optic sensor capable of measuring pressure, vibration, or both simultaneously. To address the limited sensitivity of conventional diaphragm-based designs, the sensor incorporates an optimized corrugated tube that balances the conflicting stiffness requirements for pressure and vibration measurements. The corrugated tube, acting as a mechanical transducer, is integrated with an extrinsic fiber-optic Fabry–Perot interferometer (EFPI). The EFPI cavity is formed between a reflective surface at the sealed end of the corrugated tube and the cleaved end face of an optical fiber fixed within a mounting assembly. In this configuration, displacement of the corrugated …

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …