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.