Lifang Jiao
Papers
1
Total Citations
3
H-Index
1
About
Lifang Jiao is a robotics researcher whose work focuses on intelligent control systems for autonomous and self-balancing robots. Her most cited paper, "Balance control of robot with CMAC based Q-learning" (2008, 3 citations), addresses the challenge of stabilizing a self-balancing two-wheel robot—a system characterized as high-order, multi-variable, nonlinear, strongly coupled, and inherently unstable. Jiao’s key contribution lies in proposing a reinforcement learning algorithm that leverages multiple parallel Cerebellar Model Articulation Controller (CMAC) neural networks to achieve effective balance control. This innovative approach integrates Q-learning with CMAC’s function approximation capabilities, enabling the robot to learn and adapt its control policy in real time. While her citation count reflects a focused impact, Jiao’s work is notable for advancing the application of neural network-based reinforcement learning to complex, unstable robotic systems. Her research bridges computational intelligence and practical robotics, offering insights for students and researchers interested in adaptive control, autonomous navigation, and bio-inspired learning algorithms.
Research Focus
Key Achievements
Top Papers
- 1Balance control of robot with CMAC based Q-learning3 citations · 2008