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About

Likang Feng is a rising researcher in stochastic nonlinear control theory and its application to robotic systems. Their work focuses on developing robust control strategies for robots operating under random disturbances, including stochastic noise, unknown covariance noise, and full state constraints. Feng’s major contributions include the design of command filter-based adaptive fuzzy tracking control for stochastic robotic systems, which ensures stability and performance even with full state constraints—a critical advancement for safe human-robot interaction. Their research on semi-global practical stability of random systems provides a theoretical foundation for controlling robots in uncertain environments. Feng has also pioneered pulse width modulation (PWM) control for Mecanum-wheeled mobile robots under random noise, offering a smooth voltage controller that enhances practical deployment. With over 24 citations across their most-cited works, Feng’s impact is growing, particularly in multi-stage trajectory tracking and maneuvering control of stochastic systems. Their 2025 paper on PWM control of Mecanum-wheeled robots represents a notable achievement, bridging theory and real-world application. For students and researchers, Feng’s work offers essential tools for designing resilient, adaptive controllers in stochastic robotics.