Martin Enqvist

Papers

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About

Martin Enqvist’s research sits at the intersection of automatic control, system identification, and robotics, with a particular focus on improving the performance and modeling of industrial manipulators. A central theme of his work is the development of practical, data-driven methods for identifying the dynamic parameters of robotic systems, especially their nonlinear joint stiffness. In his 2023 paper, he introduced a fast, easy-to-use process for experiment design that enhances model quality by combining prior knowledge with optimized measurement data, a contribution that directly supports the model-based control systems essential to modern industrial robots. His 2024 work further advanced this area by demonstrating how statistical linearization—using histograms of measured motor torques—can quickly and accurately determine nonlinear joint stiffness through optimized data acquisition. Earlier, Enqvist contributed to engineering education, exploring how the CDIO (Conceive-Design-Implement-Operate) framework could be applied to automatic control project courses. While his citation counts are modest, his recent methodological innovations are gaining traction in the robotics community, offering engineers practical tools for improving robot identification and control.