Adaptive Position Tracking Controller for Uncertain Parallel Robot System Based on Hybrid Brain Emotion and Cerebellar Model Articulation Control Network
Key Points
Enhanced position tracking observed with adaptive control mechanisms, improving robot stability and precision.
The controller achieves up to a 15% increase in tracking accuracy under uncertain conditions as compared to conventional methods.
Hybrid control integrating brain emotion and cerebellar model frameworks was utilized in real-time robot operations.
This study highlights the importance of advanced control strategies in parallel robot systems, which may enable better performance in dynamic environments.
Adaptive Position Tracking Controller for Uncertain Parallel Robot System Based on Hybrid Brain Emotion and Cerebellar Model Articulation Control Network | Synapse