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About

Yong Feng is a prominent control systems researcher whose career has been defined by pioneering contributions to sliding mode control theory and its applications in robotic systems. Working at the intersection of nonlinear control, machine learning, and robotics, Feng has devoted decades to solving some of the most persistent challenges in robust controller design, particularly for robotic manipulators operating under dynamic uncertainty. His most influential work addresses the singularity problem inherent in conventional terminal sliding mode control, proposing global non-singular solutions that guarantee finite-time convergence — a foundational contribution that continues to shape the field. Building on this, Feng advanced adaptive fast terminal sliding mode approaches that intelligently estimate system uncertainty bounds, while introducing chattering-reduction techniques critical for real-world deployment. His 2018 full-order sliding mode controller further demonstrated his commitment to practical applicability by delivering continuous output signals suitable for direct implementation. More recently, Feng has embraced artificial intelligence, integrating deep convolutional neural networks with fractional-order terminal sliding mode control, earning 53 citations and reflecting the field's evolution. With works spanning support vector machine-based learning control and flexible manipulator systems, his cumulative impact across more than two decades establishes him as a highly influential voice in intelligent, robust control for autonomous robotic systems.