AI-Based Embedded Controllers for Precision Motion Systems
TL;DR
An embedded controller architecture based on AHRIAS—the AI-based embedded hierarchy for high-dynamic precision motion systems, integrating sensor fusion, edge intelligence, neural-network-based prediction technique and adaptive control algorithms is presented.
Abstract
Precision motion systems play a fundamental role in intelligent automation and robotics engineering, where accurate positioning, high-speed response, and robust disturbance rejection are essential for industrial productivity. Conventional embedded motion controllers based on proportional-integral-derivative (PID), model predictive control (MPC), and adaptive control have demonstrated reliable performance under structured operating conditions. Nevertheless, increased system complexity, nonlinear dynamics, highly dynamic and uncertain loads, and random environmental conditions limits the ability of traditional controllers to achieve excellent performance in state-of-the-art robotic systems. With the significant progress of artificial intelligence (AI) in recent years, embedded controllers have evolved to include machine learning algorithms with the ability to learn dynamic behaviours of systems, predict system responses and optimize control parameters on the fly. This paper has presented an embedded controller architecture based on AHRIAS—the AI-based embedded hierarchy for high-dynamic precision motion systems, integrating sensor fusion, edge intelligence, neural-network-based prediction technique and adaptive control algorithms. The framework is composed of embedded processors, motor drivers, encoder feedback (external), IMUs and AI inference modules — facilitating higher positioning accuracy with lower response latency. The experimental characterization indicates that our scheme outperforms conventional cnclosed-loop embedded controllers in tracking performance, response time, energy efficiency and robustness against disturbances. In addition, the discussion regarding implementation difficulties, computational limitations and identification of research opportunities towards reinforcement learning, digital twins and collaborative robotic applications are elaborated in this study. The proposed framework contributes toward intelligent, adaptive, and scalable embedded motion control suitable for Industry 4.0 manufacturing environments.