Yi‐Long Li

About

Yi-Long Li is a robotics researcher specializing in advanced control systems for mobile and self-balancing robots, with a particular focus on tensor product (TP) model transformation and parallel distributed compensator (PDC) control methods. His most cited work, "Control Design of GOOGOL’s Mobile Two-Wheeled Self-Balancing Robot Based on TP Model Transformation and Non-fixed-time Step Sampling Method" (2021), addresses a critical limitation in high-dimensional linear variable parameter (LVP) models: the exponential increase in computational burden as dimensionality grows. By proposing a non-fixed-time step sampling method, Li offers a more efficient approach to control design, expanding the practical applicability of TP-based methods for real-time robotic systems. While his citation count is currently modest, his contribution is notable for tackling a fundamental bottleneck in control theory—balancing precision with computational feasibility. This work is particularly relevant for researchers in robotics, mechatronics, and nonlinear control, as it bridges theoretical modeling with hardware implementation. Li’s research underscores a commitment to making complex control strategies viable for autonomous platforms, a key step toward more responsive and stable mobile robots.

Research Focus

Key Achievements

Top Papers

1. 1
   Control Design of GOOGOL’s Mobile Two-Wheeled Self-Balancing Robot Based on TP Model Transformation and Non-fixed-time Step Sampling Method

   2 citations · 2021

Key Collaborators

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