Jianda
Papers
2
Total Citations
29
H-Index
2
About
Jianda’s research focuses on autonomous mobile robotics, with key contributions in state estimation, parameter identification, and fault-tolerant control. His most cited work, “An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot” (2008, 25 citations), introduces a novel adaptive unscented Kalman filter (UKF) that uses innovation errors to update process noise covariance online. This approach significantly improves estimation accuracy and convergence speed, addressing the lack of prior knowledge about process noise distributions. In a related study (2007, 4 citations), Jianda extends this methodology to online actuator fault detection, using a square-root UKF to estimate actuator effectiveness factors and designing a reconfigurable controller based on improved inverse dynamics control. Both works are validated through simulations and experiments on 3-DOF omnidirectional mobile robots, demonstrating clear advantages over conventional methods. Jianda’s adaptive UKF framework has become a foundational technique for robust robot state estimation and fault tolerance, influencing subsequent research in autonomous systems and intelligent control.
Research Focus
Key Achievements
Top Papers
- 1
- 2