About

Venkatakrishnan Iyer is a faculty member in the Department of Aerospace Engineering at Penn State, which has a distinguished record of research excellence and scholarship. The department's research spans traditional disciplines associated with aeronautics and astronautics, with notable strengths in rotorcraft and aero-acoustics. The department is also expanding into new areas driven by increasing computational power for design, analysis, and on-board autonomy, as well as addressing emerging industry challenges such as sustainable aviation and space systems growth. The department's accomplishments include leading initiatives such as the Vertical Lift and Rotorcraft Center of Excellence, participation in large multidisciplinary research efforts including NASA University Leadership Initiative, MURI, and SURI, and receiving numerous awards and recognitions. Faculty members, including junior faculty, have received prestigious awards such as NSF CAREER and DoD Young Investigator awards, and many are Fellows of prominent societies including AIAA, the Vertical Flight Society, and the American Physical Society. The department's faculty are also recognized through distinguished society elections, memberships in the International Academy of Astronautics, and awards such as the AIAA Aero Acoustics Award, Sperry Award, and the Collier Trophy. The department actively collaborates across the university and with external research labs, supporting a vibrant research environment focused on advancing aerospace technology and education.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Aerospace engineering
• Simulation
• Mathematics
• Automotive engineering
• Control engineering
• Physics
• Aeronautics

Selected publications

• Kinematically Constrained Joint Pose Estimation of a UAS and Non-Cooperative Moving Platform

  2026-01-08

  article1st authorCorresponding

  This work presents a Kinematically-Constrained VIO (KC-VIO) framework for jointly estimating the pose of an Uncrewed Aerial System (UAS) and a non-cooperative moving platform in GPS-denied environments. While standard Visual-Inertial Odometry (VIO) approaches model targets as unconstrained point masses governed by Random Walk (RW) processes, these models fail to capture physical motion constraints, leading to inconsistent state estimates and heading drift. To address this limitation, we fuse stereo vision and inertial measurements within an Extended Kalman Filter (EKF) that incorporates a kinematic bicycle model of the platform. By introducing a novel steering-angle state, we explicitly enforce non-holonomic constraints (zero side-slip) and couple the target’s linear and angular velocities, effectively constraining the evolution of the relative heading without requiring external global measurements. The formulation leverages both stationary and platform-based visual features and accounts for the cross-covariance between UAS and platform errors. Evaluation using real-world flight data demonstrates that, compared to standard Random Walk and simple Non-Holonomic Constraint models, the proposed kinematic formulation significantly reduces relative heading error and improves estimator consistency.

  PublisherDOI

• Vision-Based Integrated Pose Estimation of UAS and Moving Platforms

  2025-01-03 · 3 citations

  article1st authorCorresponding

  This work uses visual-inertial methods to estimate the integrated pose of an Unmanned Aerial System (UAS) and a non-cooperative moving platform in a GPS-denied environment. A single integrated navigation filter comprising an Extended Kalman Filter (EKF) is used to estimate the states of the UAS and platform. Stereo vision is used to compute the depth of feature points used in the estimation process. A generic process model is adopted for the platform that allows the pose estimation algorithm to be agnostic to the type of platform. The filter is also capable of estimating the shape of the platform. The algorithm can be used for applications such as autonomous ship deck landing, station keeping over a ship, tracking and following a moving vehicle, and obstacle avoidance. While tests are performed in a GPS-denied environment, the system can use GPS data, when available, to improve the filter performance, thus allowing both indoor and outdoor operations. Flight test results of the integrated pose estimation algorithm in an indoor setting are presented with the platform moving over a level terrain.

  PublisherDOI

• Correction: Versatile UAS Platform for Indoor Law Enforcement and First Responders Applications

  2025-01-06

  article1st authorCorresponding
  PublisherDOI

• Versatile UAS Platform for Indoor Law Enforcement and First Responders Applications

  2025-01-03

  article1st authorCorresponding

  The Penn State Autonomous Robotics Competition Club has developed a low-cost UAS platform to support law enforcement and first responders conducting indoor operations. The solution consists of single/multi-vehicle configurations, allowing for the distribution of tasks and addressing different aspects using different vehicles; for example, one UAS can perform reconnaissance while another UAS can simultaneously perform 3D mapping. The design of the mapping UAS vehicle is focused on providing a cost-effective solution with autonomous vision-based navigation, navigation in low light conditions, including thermal and night vision capabilities and two-way audio communications. The proposed solution is capable of operations including 2D and 3D mapping, indoor Search and Rescue (SAR) operations, and reconnaissance. The reconnaissance vehicle is designed to have low deployment time and provide a live video stream. A quadcopter configuration provides a viable balance between endurance and adequate thrust for both UAS to safely maneuver through the indoor environment. The proposed solution offers a versatile configuration capable of numerous applications, providing necessary situational awareness and flexibility to first responders.

  PublisherDOI

• Lift-Plus-Cruise Aircraft Modeling, Guidance, Adaptive Control, and Flight Simulation

  2024

  • Computer Science
  • Aeronautics
  • Computer Science

  In the rapidly evolving sectors of Unmanned Aircraft Systems (UAS) and Urban Air Mobility (UAM), there is an increasing need for technologies that can cater to both Department of Defense (DoD) and commercial applications. These applications range from logistics and supply delivery to disaster relief, search and rescue operations, air taxi services, and operations in underdeveloped areas. This paper presents the development and testing of an adaptive flight control system for lift-plus-cruise eVTOL aircraft, aimed at providing a smooth transition from hover/low-speed flight to forward/high-speed flight. The proposed framework consists of the aircraft model, the guidance system that translates pilot inceptor commands to control inputs, and the neural network adaptive controller that stabilizes the aircraft. The simulation is performed to demonstrate the robustness and adaptability of the system for UAM in diverse operational contexts.

  PublisherDOI

• Observer Controller Identification of a Medium-Weight Co-axial Octocopter

  AIAA SCITECH 2022 Forum · 2022 · 1 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Control engineering

  View Video Presentation: https://doi.org/10.2514/6.2022-1083.vid In this work, system identification of the open-loop airframe characteristics of a medium-weight co-axial octocopter is performed using closed-loop simulated data. Data is generated from the closed-loop octocopter model at hover using moments and thrust as excitation signals. Observer/Controller Identification (OCID) is then used to estimate the observer, controller and system Markov parameters from the generated data. The Markov parameters are used to form the Hankel matrices, that form the basis of the Eigensystem Realization Algorithm (ERA) used to determine the minimal realization of the system matrices. The effect of noise on the system identification process is analyzed by computing the Mode Singular Values (MSV). The use of Eigensystem Realization Algorithm with Data Correlations (ERA/DC) is also explored to demonstrate the improvement in system identification in the presence of noise. The system identification process presented in this work can be used to determine the system’s modes that help in analyzing the handling qualities of the aircraft, which can be useful in improving simulation model fidelity and estimating higher order dynamics.identification process presented in this work can be used to determine the system’s modes that help in analyzing the handling qualities of the aircraft which, in turn, can be useful in improving simulation model fidelity and estimating higher order dynamics.

  PublisherDOI

• Compound Rotorcraft Yaw Control Fault Detection

  Proceedings of the Vertical Flight Society 78th Annual Forum · 2020

  • Computer Science
  • Engineering
  • Aerospace engineering

  Emerging vertical flight concepts being proffered for solutions to the Future Vertical Lift (FVL) mission set such as compound high speed rotorcraft can be designed with multiple, coupled control effectors thus creating redundant systems in one or two more axes to generate control forces and moments which allow for a range of trim states. In the FVL mission area future rotorcraft will be asked to fly into high threat environments where potential failure modes can be encountered due to enemy fire or mechanical failure causing reduction of the safe flight envelope. Fault detection creates options to increase the survivability of the crew and passengers allowing an emergency flight envelope to be proposed. One of the more serious potential failures due to enemy fire is a loss of yaw control. Faults in yaw control can be detected in a compound rotorcraft with a vectored thrust ducted propeller (VTDP) or similar anti-torque thruster. An online Kalman filter (KF) for a dimensional yaw moment coeff icient model will be used to estimate vehicle yaw coeff icients. Deviation from the nominal coefficients will be monitored based on the KF statistics in the case of both rudder and tail rotor failure at 60, 40, and 20 ft/s in forward flight. Both frozen zero rudder and ganged sector faults as well as failed tail rotor faults were successfully detected at all airspeeds except the failed tail rotor at 60 ft/s. For the yaw control faults considered, post fault excitation appears airspeed dependent. An online KF estimator for yaw control fault detection could successfully be integrated into the design of a compound rotorcraft with VTDP thereby increasing system safety.

  PublisherDOI

• Nonlinear Yaw Control of a Compound Helicopter

  2020

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence
  Publisher

Frequent coauthors

• Jeffrey A. Lewis

  University of Arkansas at Fayetteville

  2 shared

• Eric N. Johnson

  Google (United States)

  2 shared

• Joseph Horn

  1 shared

• Puneet Singla

  Pennsylvania State University

  1 shared

• Brian Taylor

  Sandia National Laboratories California

  1 shared

• Thanakorn Khamvilai

  Pennsylvania State University

  1 shared

• Eric S. Johnson

  1 shared

Labs

• Aerospace Engineering Research at Penn StatePI

Awards & honors

• AIAA Aero Acoustics Award (3)
• AIAA Sperry Award (2)
• AIAA Applied Aerodynamics Award
• Am Astronautical Society Brouwer Award
• AIAA de Florez Award in Flight Simulation