Jacob Goodman

University of Maryland, Baltimore County

Papers

Total Citations

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About

Jacob Goodman’s research centers on nonlinear estimation and control strategies for robotic systems, with a particular focus on improving the accuracy and robustness of manipulator dynamics. His major contributions lie in the comparative analysis of estimation techniques, notably the extended Kalman filter (EKF) and robust sliding-mode observers, applied to RRR (revolute-revolute-revolute) robotic manipulators. In his most-cited work, "A Variable Structure-Based Estimation Strategy Applied to an RRR Robot System" (2017, 14 citations), Goodman demonstrates how variable-structure methods can outperform classical approaches in handling system uncertainties and nonlinearities—a critical advancement for reliable robot control in real-world environments. A related study (2017, 3 citations) further explores these nonlinear estimation strategies, reinforcing their practical value. Though his citation counts are modest, Goodman’s work is notable for bridging theoretical estimation frameworks with applied robotics, offering clear, implementable solutions for engineers. His research is especially relevant for students and practitioners seeking robust, computationally efficient methods for precise robotic manipulation.