About

Gang Tao is a Professor of Electrical Engineering and a Fellow of the IEEE. He holds a Ph.D. in Electrical Engineering and is affiliated with the University of Virginia. His research focuses on multivariable adaptive control, with particular emphasis on adaptive actuator failure compensation, adaptive actuator nonlinearity compensation, and adaptive sensor uncertainty compensation. He leads the Adaptive Systems and Control Research Group, contributing to advancements in adaptive control methodologies. In addition to his research, Professor Tao has teaching experience in courses such as adaptive control and linear control systems, reflecting his commitment to education in the field of electrical engineering.

Research topics

• Artificial Intelligence
• Computer Science
• Transport engineering
• Chemistry
• Engineering
• Polymer chemistry
• Computer network
• Composite material
• Materials science
• Chemical engineering
• Nanotechnology
• Nuclear chemistry
• Control engineering
• Real-time computing

Selected publications

• Full-Scale Laboratory Test of Distributed Fiber Optic System for Well Integrity Monitoring

  2025-11-30

  article1st authorCorresponding

  Abstract Distributed fiber optic (FO) technology offers the benefit of continuous downhole monitoring without frequent well entries for conventional well inspections, which not only introduce additional well integrity risk due to the invasive nature of the workover activities but also add significant cost to operators. A comprehensive research program sponsored by the Pipeline Research Council International, Inc., the Solution Mining Research Institute, and the US Department of Transportation, Pipeline and Hazardous Materials Safety Administration was executed to assist underground gas storage (UGS) operators in improving their ability to make informed decisions regarding incorporating FO technology into UGS well integrity monitoring. As a major component in this research program, full-scale laboratory tests were executed to experimentally evaluate the performance of FO monitoring systems in detecting various downhole events that pose well integrity threats. Distributed strain sensing (DSS) and distributed acoustic sensing (DAS) systems provided by two commercial vendors were tested. Two test wells with cemented pipe-in-pipe configuration were purposely built for the DSS and DAS tests. FO cables were attached to the inner pipe before they were cemented in-place. The DSS test specimen was subjected to various axial tensile loads. Strain gauge measurements along the inner pipe were used to evaluate the DSS measurements. The DAS test well was designed with features that allowed for simulating gas leaks through various leak paths. The DAS measurements were compared with the known leak events to assess the DAS system’s performance. Overall, the test results provide a comprehensive understanding of the capabilities and limitations of the DSS and DAS systems for monitoring various downhole anomalies. The outcome of this research program has established a technical basis for future implementation of distributed FO monitoring system as an alternative well integrity monitoring method to enhance the safety and efficiency of operations.

  PublisherDOI

• Adaptive output tracking control with reference model system uncertainties

  Automatica · 2025-01-31 · 5 citations

  articleOpen access1st authorCorresponding

  This paper develops new adaptive output tracking control schemes with the reference output signal generated from an unknown reference system whose output derivatives are also unknown. To deal with such reference system uncertainties, an expanded adaptive controller structure is developed to include a parametrized estimator of an equivalent reference input signal. Without using the knowledge of the reference system transfer function and equivalent input, both are the critical components of a traditional model reference adaptive control (MRAC) scheme, the new MRAC schemes, developed for various cases of plant and reference model uncertainties, ensure completely parametrized error equations and globally stable parameter adaptation, leading to the desired closed-loop system stability and asymptotic output tracking properties.

  PublisherDOI

• Casing integrity diagnosis using wellhead-based electromagnetic time domain reflectometry: laboratory study

  Structural Health Monitoring · 2025-01-31

  articleOpen access

  In this study, extensive double-blind laboratory experiments were conducted to evaluate a novel, noninvasive method for diagnosing casing integrity using electromagnetic time-domain reflectometry (EM-TDR). This method transmits a high-frequency electromagnetic pulse into the casing and records reflected signals from integrity-related impedance changes at the wellhead. The experiments included three casing sizes—7, 5.5, and 4.5 inches in diameter—with various machined features and natural corrosion to simulate casing degradation. Over 60% of the features were less than 50% of the casing thickness, and more than 65% were narrower than a 45° azimuthal angle on the casing’s outer surface. All features larger than 1 cubic inch were successfully detected using EM-TDR. For machined features smaller than 0.1 cubic inch, identification rates were 94%, 90%, and 93% for the 7-, 5.5-, and 4.5-inches casings, respectively. For features between 0.1 and 1 cubic inch, detection rates were 90%, 89%, and 83%, respectively. In the cemented segment of the 4.5-inch casing, 50% of features between 0.1 and 1 cubic inch were identified, and reflections from the cement–air interface were also observed. Among natural corrosion features, 63.5% were detected. These results indicate that the EM-TDR method can serve as a rapid screening tool to complement existing high-resolution imaging or logging methods for monitoring casing integrity.

  PublisherDOI

• PR244-231111-R01 Implementing Fiber Optic Technology for Underground Gas Storage Well Monitoring

  2025-10-10

  reportOpen access1st authorCorresponding

  Conventional well integrity inspection requires frequent well entry, which not only introduces additional well integrity risks due to the invasive nature of the workover activities but also adds significant cost to the inspection process. Taking active underground gas storage (UGS) wells out of service for direct inspection can also strain natural gas deliverability and the ability of the storage system to meet the market demand. Distributed fiber optic sensing (DFOS) technology offers the benefit of continuous measurement along the entire length of the fiber optic cable without frequent well entry and may potentially reduce the cost in well integrity management. This report describes a comprehensive research program that investigated various DFOS technologies through a literature review, a review of past research programs, and full-scale laboratory tests. A comprehensive understanding of the capabilities and limitations of various DFOS technologies for UGS well monitoring applications was established. This should assist UGS operators in developing a reliable solution for using DFOS technology as an alternative well integrity monitoring method that allows the intervals for mechanical integrity tests or traditional well inspections, as specified by regulators, to be extended. This document aims to provide technical references to assist UGS operators improve their ability to make informed decisions regarding incorporating DFOS technologies into UGS well integrity monitoring.

  PublisherDOI

• A Discrete-Time Least-Squares Adaptive State Tracking Control Scheme With a Mobile-Robot System Study

  IEEE Transactions on Automatic Control · 2025-06-16 · 1 citations

  articleSenior author

  This paper develops an adaptive state tracking control scheme for discrete-time systems, using least-squares algorithms, as the new solution to the long-standing discrete-time adaptive state tracking control problem. The system stability and state tracking properties are proved mathematically. The developed adaptive state tracking control scheme, combined with a newly proposed collision avoidance mechanism, is applied to a multi-robot system to achieve tracking objectives. Simulation results demonstrate its effectiveness in achieving state tracking and collision avoidance.

  PublisherDOI

• GMATP-LLM: A General Multi-Agent Task Dynamic Planning Method using Large Language Models

  2025-07-28

  article

  In this work, we introduce a generalized task planning innovation framework named GMATP-LLM, which is designed for multi-agent systems. This method utilizes Chain-of-Thought (CoT) prompting to enable Large Language Models (LLMs) to perform task decomposition and assignment processes, transforming high-level task instructions into a set of sub-tasks. Based on the assignment strategy, it generates a PDDL goal plan, which is solved by an intelligent planner to generate a sequence of actions. The framework introduces a multi-agent three-dimensional Spatio-Temporal Motion Corridor (STMC) to constrain and optimize the parallel motion of the agents, improving system efficiency. This method combines the reasoning capabilities of LLMs and the fast solving advantage of the intelligent PDDL planners. It has been verified through simulation and real-world experiments across various task categories, achieving favorable results in multi-agent task planning.

  PublisherDOI

• A Discrete-Time Least-Squares Adaptive State Tracking Control Scheme with A Mobile-Robot System Study

  arXiv (Cornell University) · 2024-03-25 · 1 citations

  preprintOpen accessSenior author

  This paper develops an adaptive state tracking control scheme for discrete-time systems, using the least-squares algorithm, as the new solution to the long-standing discrete-time adaptive state tracking control problem to which the Lyapunov method (well-developed for the continuous-time adaptive state tracking problem) is not applicable. The new adaptive state tracking scheme is based on a recently-developed new discrete-time error model which has been used for gradient algorithm based state tracking control schemes, and uses the least-squares algorithm for parameter adaptation. The new least-squares algorithm is derived to minimize an accumulative estimation error, to ensure certain optimality for parameter estimation. The system stability and output tracking properties are studied. Technical results are presented in terms of plant-model matching, error model, adaptive law, optimality formulation, and stability and tracking analysis. The developed adaptive control scheme is applied to a discrete-time multiple mobile robot system to meet an adaptive state tracking objective. In addition, a collision avoidance mechanism is proposed to prevent collisions in the whole tracking process. Simulation results are presented, which verify the desired system state tracking properties under the developed least-squares algorithm based adaptive control scheme.

  PublisherOA PDFDOI

• Real-Time Monitoring of Underground Gas Storage Wells Using Distributed Fiber Optic Sensing: An Experimental Validation

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Real-Time Terrain-Aware Path Optimization for Off-Road Autonomous Vehicles

  2024-06-02 · 4 citations

  article

  Navigating off-road terrains is crucial for military, agricultural, and rescue operations. Existing algorithms for off-road path planning offer limited adaptability to complex terrains and often lack the computational efficiency required for real-time applications. This is largely due to the nonconvex and nonsmooth characteristics of terrain geometry. Our research introduces an innovative terrain representation technique that streamlines the complexity of the terrain into a manageable path optimization problem, focusing on optimizing vehicle attitude concerning the path. By employing discrete curves to represent lateral terrain elevation changes, our method facilitates the direct integration of vehicle attitude into the optimization framework, thereby diminishing the need for computationally intensive traversability maps typical of traditional approaches. We tackle the resulting nonlinear optimization problem with a constrained iterative linear quadratic regulator (iLQR), achieving real-time path planning capabilities. The proposed method demonstrates improved computational efficiency and enhanced path quality, demonstrating significant time savings in planning while ensuring high-quality outcomes.

  PublisherDOI

• Adaptive Output Tracking Control with Reference Model System Uncertainties

  arXiv (Cornell University) · 2024-06-08

  preprintOpen access1st authorCorresponding

  This paper develops adaptive output tracking control schemes with the reference output signal generated from an unknown reference system whose output derivatives are also unknown. To deal with such reference system uncertainties, an expanded adaptive controller structure is developed to include a parametrized estimator of the equivalent reference input signal. Without using the knowledge of the reference system transfer function and equivalent input, both are the critical components of a traditional model reference adaptive control (MRAC) scheme, the developed new MRAC schemes designed for various cases plant and reference model uncertainties, ensure completely parametrized error systems and stable parameter adaptation, leading to the desired closed-loop system stability and asymptotic output tracking.

  PublisherOA PDFDOI

Recent grants

• Adaptive Failure Compensation for Performance-Critical Control Systems

  NSF · $220k · 2006–2010

• Adaptive Fault Accommodation Based Resilient Control Techniques

  NSF · $348k · 2015–2019

Frequent coauthors

• Bin Jiang

  Nanjing University of Aeronautics and Astronautics

  58 shared

• Ruiyun Qi

  53 shared

• Suresh M. Joshi

  51 shared

• Xidong Tang

  General Motors (United States)

  38 shared

• Pétros Ioannou

  38 shared

• Chang Tan

  Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital

  33 shared

• Hao Yang

  Kyoto University

  32 shared

• Bin Jiang

  29 shared

Awards & honors

• Fellow of IEEE