About

Robert Skelton is a TEES Distinguished Research Professor in the Department of Aerospace Engineering at Texas A&M University. His research interests encompass interdisciplinary research with a focus on tensegrity systems and the integration of structure and control design. His expertise includes system identification, specifically deriving models from data, which is a key aspect of his work. As a distinguished faculty member, he contributes significantly to advancing knowledge in aerospace engineering through his research and academic leadership.

Research topics

• Computer Science
• Physics
• Engineering
• Mathematics
• Mathematical analysis
• Structural engineering
• Artificial Intelligence
• Aerospace engineering
• Astronomy
• Architectural engineering
• Operating system
• Environmental resource management
• Environmental science
• Ecology
• Astrobiology
• Classical mechanics

Selected publications

• Intraspecific variation in drought tolerance traits in a Nothofagus species (southern beech) in southern South America

  Forest Ecology and Management · 2025-09-22 · 1 citations

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• Tensegrity system dynamics in fluids

  Nonlinear Dynamics · 2025-03-22 · 2 citations

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• Dynamics and Control of Aerospace Systems

  2025-03-12

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  This reprint offers a comprehensive collection of cutting-edge research in the dynamics, control, and actuation of aerospace systems, addressing critical challenges and innovative solutions within aerospace engineering. By integrating novel methodologies and practical applications, this reprint showcases advancements in distributed control for space manipulators, state-dependent control for drag-free satellites, hybrid propulsion systems for interplanetary CubeSats, and advanced strategies for aero-engine and spacecraft control. A diverse range of techniques, including sliding mode control, model predictive control, decentralized LQR, and adaptive fuzzy control, are explored to achieve robust solutions for trajectory tracking, vibration suppression, and integrated guidance and control. Furthermore, this reprint highlights the transformative potential of advanced materials and sensing technologies, such as piezoelectric sensors, fiber Bragg grating (FBG) systems, and smart materials, in enhancing vibration suppression, structural health monitoring, and system reliability. Through a combination of theoretical modeling, computational analysis, and experimental validation, the studies provide a holistic perspective on the design and optimization of aerospace systems. Aimed at researchers, engineers, and professionals, this reprint serves as an invaluable resource for understanding the latest advancements and future directions in aerospace dynamics, control, and actuation technologies.

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• Minimal mass design of a tensegrity tower for lunar electromagnetic launching

  Acta Astronautica · 2024-12-13 · 9 citations

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• A Markov data-based approach to system identification and output error covariance analysis for tensegrity structures

  Nonlinear Dynamics · 2024-03-19 · 7 citations

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• Sensor Fault Detection Approach to Tensegrity Structures Using Markov Parameters

  2024-10-10

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  This paper presents a sensor fault detection method based on output error covariance and demonstrates its efficacy on tensegrity structures. An approximation model of the fault system is developed first using input and output signals. Subsequently, this fault system is compared with a reference system, and their output covariance is analyzed using the Markov parameters of both systems. In addition, an algorithm is presented to identify the fault sensor channels from the output error covariance. An examination of a tensegrity double prism tower, assuming fault sensors producing zero-mean Gaussian white noise, is conducted. The result validates the effectiveness of this approach in pinpointing the malfunctioning sensor channels. This proposed approach is adaptable to other structural applications of fault sensor identification.

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• Statics and dynamics of pulley-driven tensegrity structures with sliding cable modeling

  Applied Mathematical Modelling · 2024-03-07 · 16 citations

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• Model-Based and Markov Data-Based Linearized Tensegrity Dynamics and Analysis of Morphing Airfoils

  2024-01-04 · 7 citations

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  This paper introduces two methods for linearizing nonlinear tensegrity dynamics: a model-based and a Markov data-based approach. We first give the tensegrity notations and their nonlinear dynamics, followed by the theoretical formulation of a model-based linearization using Taylor expansion. Subsequently, the paper presents an empirical method of Markov data-based approach to linearizing these dynamics. Finally, we implement a shape-controllable tensegrity airfoil as an example. An extensive study and analysis are provided to compare the efficacy of both linearization methods. The principles established in this research are applicable to a variety of structures beyond tensegrity.

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• Finite word-length optimal simulation for high-dimensional dynamical systems: Examples of tensegrity structures

  Mechanical Systems and Signal Processing · 2024-04-16 · 10 citations

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• Q-Markov Covariance equivalent realizations for unstable and marginally stable systems

  Mechanical Systems and Signal Processing · 2023-04-10 · 9 citations

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Frequent coauthors

• Muhao Chen

  Texas A&M University

  78 shared

• Raman Goyal

  50 shared

• Manoranjan Majji

  Texas A&M University

  42 shared

• Karolos Grigoriadis

  36 shared

• Guoming Zhu

  Michigan State University

  34 shared

• Fernando Fraternali

  University of Salerno

  33 shared

• Maurı́cio C. de Oliveira

  Universidade Estadual de Campinas (UNICAMP)

  31 shared

• Gerardo Carpentieri

  22 shared