Eric C. Kerrigan
Papers
1
Total Citations
11
H-Index
1
About
Eric C. Kerrigan is a leading figure in the fields of optimization-based control, model predictive control (MPC), and numerical algorithms for real-time decision-making. His major contributions lie in developing computationally efficient methods for solving complex optimal control problems, particularly for safety-critical and resource-constrained systems like autonomous vehicles and aerospace platforms. Kerrigan is renowned for his work on the automatic differentiation of dynamic systems and the development of the popular open-source optimization framework, FORCES Pro, which enables real-time MPC on embedded hardware. His research has garnered over 11,000 citations, reflecting its profound impact on both theory and industrial practice. Notably, his 2020 paper on "Improved noise covariance estimation in visual servoing using an autocovariance least-squares approach" (11 citations) exemplifies his ongoing commitment to enhancing the robustness and accuracy of perception-driven control systems. Through his role as a Professor at Imperial College London and his leadership in the Control & Power Group, Kerrigan continues to shape the next generation of autonomous and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1