About

Cornel Sultan is a Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He holds a Ph.D. in Aerospace Engineering from Purdue University, an M.S. in Applied Mathematics from Purdue University, and a B.S./M.S. in Aerospace Engineering from Polytechnic University, Romania. His research expertise includes Dynamics, Control, and Structures, with a focus on autonomous systems, vehicle dynamics and control, spacecraft attitude dynamics and control, and stability and control. Professor Sultan has been a faculty member at Virginia Tech since 2007, progressing from Assistant to Full Professor. His professional history also includes roles as a Staff Engineer at United Technologies Research Center, a Research Engineer at SSCI, and a Research Fellow at Harvard University Medical School. He has served as Technical Area Chair for the AIAA GNC Conference on Aerospace Robotics and Unmanned/Autonomous Systems in 2016 and on Control Theory, Analysis, and Design in 2015, and is a Fellow of the Virginia Tech College of Engineering and an AIAA Associate Fellow. His contributions to the field are recognized through his leadership in research and his involvement in professional societies.

Research topics

• Computer Science
• Mathematical optimization
• Artificial Intelligence
• Mathematics
• Engineering
• Mechanical engineering
• Oceanography
• Meteorology
• Control engineering
• Geology
• Environmental science
• Physics
• Seismology
• Marine engineering
• Mechanics
• Aerospace engineering

Selected publications

• Closed-Form Expressions of Coprime Factors for Second-Order Systems

  Journal of Guidance Control and Dynamics · 2026-05-12

  articleSenior author

  Coprime factors express control systems in a fractional representation. Their computation requires solutions to continuous-time algebraic Riccati equations. We provide a closed-form solution to the generalized filtering algebraic Riccati equation that leads to closed-form expressions for the coprime factors for second-order systems. Furthermore, for second-order linear time-invariant systems, these expressions can be written directly in terms of the second-order system matrices, which have structural properties. Their utility, in terms of computation time, is illustrated by designing a coprime factor-based Youla–Kucera controller for large-size systems with changing parameters. Specifically, we showed, through the examples of reconfigurable satellite networks and spring–mass–damper systems, that the closed-form expressions for coprime factors can be computed much faster than the standard way, which results in faster implementation of the Youla–Kucera controller. Hence, they are very useful for computing coprime factors for large-size systems, especially for real-time computation with limited resources.

  PublisherDOI

• Power generation maximization framework with particle swarm optimization for ocean current turbine farms

  Ocean Engineering · 2026-01-24

  article
  PublisherDOI

• H-Infinity Control Design and Entropy Minimization for Second-Order Systems

  Journal of Dynamic Systems Measurement and Control · 2025-11-29

  articleSenior author

  Abstract A modified Newton–Kleinman method is introduced to solve the algebraic Riccati equations present in the H-infinity controller synthesis problem. The new method utilizes the inherent properties of the mass, stiffness, and damping matrices associated with second-order systems to efficiently compute the solutions to the algebraic Riccati equations for large systems, with an analytical proof of convergence. This allows for efficient H-infinity controller synthesis as compared to traditional methods such as the Hamiltonian approach and the standard Newton–Kleinnman method when applied to the same systems. The entropy formulation of the H-infinity controller synthesis problem is then utilized in conjunction with the new algorithms to develop a data-driven controller that balances the total entropy and the quadratic cost functional associated with the problem.

  PublisherDOI

• Closed-Form Solutions to Continuous-Time Algebraic Riccati Equation for Second-Order Systems

  Journal of Applied Mechanics · 2024-03-13 · 8 citations

  articleOpen accessSenior author

  Abstract Two closed-form methods to solve the continuous-time algebraic Riccati equation (CARE) for second-order systems in terms of the mass, damping, and stiffness matrices are presented. One method utilizes the modal transformation of mass and stiffness matrices, and the other does not require this transformation. Hundreds of high-dimensional second-order systems are used to show that these methods achieve similar or better accuracy compared to the state-of-the-art, while significantly reducing the computation time. Furthermore, advantages of these methods are illustrated in vibration control problems.

  PublisherOA PDFDOI

• Robust Variable Horizon MPC with Move Blocking for Helicopter Shipboard Landing on Moving Decks

  2024-01-04

  articleSenior author

  A new design of robust variable horizon model predictive control (VH-MPC) with move blocking is proposed for helicopter shipboard landings on moving decks in rough seas. The design introduces an efficient strategy to implement the VH-MPC by evaluating multiple controllers at each iteration, corresponding to different horizon lengths, and selecting the one with the lowest cost. This approach avoids the computational burden of solving a batch of mixed integer quadratic programming problem for executing VH-MPC. Additionally, move blocking is introduced to reduce the number of decision variables in the MPC optimization problem. The new VH-MPC can be executed in parallel computation, which means the controller latency is determined by the slowest single optimization problem’s solution time rather than the sum of all solution times. To evaluate the design, a nonlinear helicopter-ship dynamics interface is utilized, incorporating significant helicopter and ship dynamics, as well as ship airwake interactions.

  PublisherDOI

• Continuous-Time Algebraic Riccati Equation Solution for Second-Order Systems

  Journal of Dynamic Systems Measurement and Control · 2024-06-04

  articleSenior author

  Abstract The continuous-time algebraic Riccati equation (ARE) is often utilized in control, estimation, and optimization. For a linear system with a second-order structure of size n, the ARE required to be solved to get the control values in standard control problems results in complex subequations in terms of the second-order system matrices. The computational costs of solving the algebraic Riccati equation through standard methods such as the Hamiltonian matrix pencil approach increase substantially as matrix sizes increase for a second-order system, due to the eigendecomposition of the 2n×2n system matrices involved. This work introduces a new solution that does not require the eigendecomposition of the 2n×2n system matrices, while satisfying all of the requirements of the solution to the Riccati equation (e.g., detectability, stabilizability, and positive semidefinite solution matrix).

  PublisherDOI

• Continuous Time Algebraic Riccati Equation Solution for Second Order Systems

  SSRN Electronic Journal · 2023-01-01

  preprintOpen accessSenior author
  PublisherDOI

• <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si398.svg" display="inline" id="d1e4863"><mml:mi>ν</mml:mi></mml:math>-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

  Ocean Engineering · 2023-04-18 · 12 citations

  article
  PublisherDOI

• Efficient Near-Optimal Control of Large-Size Second-Order Linear Time-Varying Systems

  IEEE Control Systems Letters · 2023-01-01 · 5 citations

  article

  Building on the two time-scale decomposition method, we propose a solution to the optimal control problem for second-order Linear Time-Varying (LTV) systems. This solution achieves convergence to that provided by standard numerical solvers such as Pontryagin’s Maximum Principle (PMP), and significantly enhances computational efficiency, making it applicable to large-size systems. We achieve this by developing closed-form solutions to the Continuous Algebraic Riccati Equations (CARE) for second-order systems. We also show through a spring-mass-damper system that our approach is significantly faster and more computationally efficient than standard methods.

  PublisherDOI

• Power Generation Maximization Control Framework for Ocean Current Turbine Farms

  2023-07-10

  article

  Abstract In this work, we propose a control framework for farms consisting of ocean current turbines (OCT). The ocean current turbine systems used in this farm are tethered to the ground of the ocean, and their depth can be adjusted online based on the maximum ocean current power available. To maximize the average power generated by the farm, the ocean current turbine wake interactions must be taken into account, and also each turbine in the farm should achieve these changes in the position reference with minimum control energy. Considering additional limitations such as keeping the tethering cables away from each other and avoiding collisions between the turbines, an advanced optimization framework is developed to achieve the maximum power generation in a specified region. Tracking of the reference trajectories by the ocean current turbine systems is achieved by model predictive control (MPC). A case study is presented to highlight the significant estimated improvement in the average energy generated by the farm using the proposed framework and control methodology.

  PublisherDOI

Recent grants

• CAREER: Bio-inspired Controllable Tensegrity Structures

  NSF · $441k · 2010–2016

• Collaborative Research: Optimized Harvesting of Hydrokinetic Power by Ocean Current Turbine Arrays Using Integrated Control

  NSF · $122k · 2013–2017

• Collaborative Research: Design and Control of Networked Offshore Hydrokinetic Power-Plants with Energy Storage

  NSF · $214k · 2018–2023

Frequent coauthors

• James H. VanZwieten

  32 shared

• Nikolaos I. Xiros

  University of New Orleans

  28 shared

• Rakesh K. Kapania

  Virginia Tech

  14 shared

• Tri D. Ngo

  University of Central Florida

  13 shared

• Yufei Tang

  Florida Atlantic University

  13 shared

• Robert E. Skelton

  Texas A&M University

  12 shared

• Broc Dunlap

  Virginia Tech

  10 shared

• Tuğrul Oktay

  10 shared

Education

• M.S., Mathematics

  Purdue University

  1999

• Ph.D., Aeronautics and Astronautics Engineering (AAE)

  Purdue University

  1999

Awards & honors

• AIAA Associate Fellow (2017)
• Fellow Virginia Tech College of Engineering (2016)