Hybrid Adaptive MPC with Edge AI for 6-DoF Industrial Robotic Manipulators

Autonomous robotic manipulators in industrial environments face significant challenges, including time-varying payloads, multi-source disturbances, and real-time computational constraints. Traditional model predictive control frameworks degrade by over 40% under model uncertainties, while conventional adaptive techniques exhibit convergence times incompatible with industrial cycles. This work presents a hybrid adaptive model predictive control framework integrating edge artificial intelligence with dual-stage parameter estimation for 6-DoF industrial manipulators. The approach combines recursive least squares with a resource-optimized neural network (three layers, 32 neurons, 1.2). This performance surpasses recent adaptive control methods while maintaining proven stability guarantees. Hardware validation under realistic industrial conditions remains necessary to confirm practical applicability.