Project Highlights
This project introduces an adaptive neural filter to improve stability and performance in teleoperation systems under random, time-varying delays. Key challenges addressed include:
- Instability from network uncertainties: Random delays in communication networks can destabilize teleoperation systems.
- Degraded performance: Delays reduce operator transparency and system usability.
- Balancing stability and performance: Conventional methods are often overly conservative, limiting performance.
Neural filter approach: An adaptive NN dynamically adjusts filter coefficients, ensures stability via Lyapunov-Krasovskii analysis, avoids local minima, and provides accurate signal estimations under uncertain delays.
Experimental results: Demonstrated outstanding performance, improved stability, and smoother teleoperation operation on a real-world platform.
Read Paper on IEEE RA-L