Papers
11
Total Citations
210
H-Index
8
About
Liyang Wang is a robotics and control systems researcher whose work sits at the intersection of machine learning, optimization, and autonomous locomotion. Best known for pioneering energy-efficient control frameworks for biped walking robots, Wang has made substantial contributions to the field by integrating support vector machine (SVM) and support vector regression (SVR) techniques with advanced state estimation methods to address one of bipedal robotics' most persistent challenges: high energy consumption during dynamic walking. His most-cited work, "Energy-Efficient SVM Learning Control System for Biped Walking Robots" (2013, 45 citations), introduced a novel weighted training approach that directly incorporates energy costs into the learning process, setting a strong precedent for resource-aware robot control. Complementary studies employing Unscented Kalman Filters (UKF) and interval type-2 fuzzy logic further demonstrate his commitment to robust, adaptable controllers under real-world uncertainty. Wang also extended his expertise to assistive robotics, contributing RGB-D sensor-based target detection for intelligent wheelchairs (2015, 28 citations). Collectively, his body of work, accumulating over 200 citations, reflects a sustained effort to make autonomous robots both smarter and more energy-conscious.
Research Focus
Key Achievements
Top Papers
- 1Energy-Efficient SVM Learning Control System for Biped Walking Robots45 citations · 2013
- 2A UKF-Based Predictable SVR Learning Controller for Biped Walking32 citations · 2013
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