Tzu-Chun Kuo

Chien Hsin University of Science and Technology

Papers

Total Citations

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About

Tzu-Chun Kuo is a control systems researcher whose work focuses on intelligent and adaptive control strategies for nonlinear and uncertain dynamic systems. His key research areas include sliding-mode control, real-time learning controllers, and robotic manipulator control. Kuo’s most impactful contribution is the development of an intelligent complementary sliding-mode control with dead-zone parameter modification, a paper that has garnered 14 citations. This work addresses the challenge of chattering and parameter uncertainty in sliding-mode control, offering a more robust and practical solution for real-world applications. In an earlier notable study, Kuo proposed a real-time learning controller for a two-link robotic arm, combining a proportional-derivative controller with a cerebellar model articulation controller (CMAC). This innovative approach used feed-forward CMAC compensation to learn and control uncertain system dynamics with unknown but bounded nonlinearities, demonstrating significant potential for improving robotic precision and adaptability. Though his citation counts are modest, Kuo’s contributions to intelligent control design provide valuable insights for researchers working on advanced robotic systems and nonlinear control theory.