Hierarchical Adaptive Control of IoT-Integrated Mechatronic Systems Using Nonlinear Optimization and Edge-Cloud Collaboration

Abstract

This study proposes a novel adaptive control framework integrating nonlinear IoT dynamics with mechatronic systems to address the challenges of strong coupling, uncertainty, and real-time constraints. Our key innovation lies in a hierarchical optimization architecture combining model predictive control (MPC) with deep reinforcement learning (DRL), enabling dynamic adaptation through edge-cloud collaboration. Experimental results demonstrate efficiency improvements from 13.79% to 83.46% in subsystem performance, highlighting the algorithm's capability to balance robustness and adaptability. This work fills a critical gap in existing methods by unifying distributed sensing, online learning, and nonlinear optimization for IoT-enabled mechatronic systems. In contrast, the presence of high efficiency values, such as 93.76% and 80.75%, in certain parts of the system indicates that the system is able to achieve efficient operation under certain specific conditions. This study proposes a novel adaptive control framework integrating nonlinear IoT dynamics with mechatronic systems, leveraging Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), and Differential Evolution (DE) within a hierarchical optimization architecture. Experimental validation on a manipulator testbed demonstrates 83.46% efficiency improvement in subsystem coordination, <100 ms response time under dynamic coupling, and 92.5% trajectory accuracy with ±5% standard deviation under 30% noise interference.