Adaptive Multimodal Control of Robotic Arm

Adaptive DRL + NMPC MATLAB code for multi-DOF robotic arms achieving 0.05 mm precision, fast response & robust real-time control.

The paper “Optimization of Adaptive Algorithm for Precise Motion Control of Multi-Degree-of-Freedom Robotic Arms” presents an advanced control framework that combines Deep Reinforcement Learning (DRL) with Nonlinear Model Predictive Control (NMPC) for achieving high-precision, fast, and stable motion control of robotic arms.

It introduces a multi-modal perception architecture using visual, force, and IMU sensors to adapt in real time to dynamic environments. The proposed method is compared with PID and fuzzy control algorithms, showing major improvements:

• Static accuracy: 0.05 mm (67% better than PID)
• Dynamic tracking error: 0.3 mm (only 25% of PID’s error)
• Force overshoot: <3% (PID ≈18%)
• Assembly success rate: 99.3%
• Fast adaptation: 150 ms stiffness change response

The paper demonstrates its effectiveness in industrial assembly, medical robotics, and precision operations, and discusses future work involving digital twin integration, quantum reinforcement learning, and edge computing for ultra-fast robotic control.