The rapid evolution of intelligent automation and cyber-physical systems has intensified the need for advanced embedded architectures capable of supporting adaptive, real-time, and resource-efficient mechatronic applications. Traditional embedded systems, although effective for deterministic control tasks, exhibit limitations in handling dynamic environments due to their rigid structures, limited computational intelligence, and inefficient resource utilization. This study presents a comprehensive design and optimization of an intelligent embedded system architecture tailored for modern mechatronic systems. The proposed architecture adopts a modular, layered framework that integrates sensing, processing, control, and actuation components with embedded artificial intelligence. Advanced control strategies, including adaptive control and machine learning-based decision-making, are incorporated to enable predictive maintenance, fault detection, and real-time system optimization. Furthermore, the study integrates key optimization techniques such as dynamic voltage scaling, memory-efficient architectures, and real-time scheduling algorithms to address critical constraints related to power consumption, memory utilization, and timing performance. Mathematical models for control dynamics, power consumption, and task scheduling are developed to support system design and evaluation. Comparative analysis demonstrates that the proposed system significantly outperforms conventional embedded systems in terms of energy efficiency, response time, adaptability, and overall system reliability. The results highlight the effectiveness of combining embedded intelligence with optimized resource management in achieving high-performance mechatronic systems. This study contributes a scalable and robust framework for the development of next-generation intelligent embedded systems and provides practical insights for applications in robotics, smart manufacturing, autonomous systems, and medical technologies. The findings align with emerging technological paradigms, including embedded artificial intelligence, edge computing, and Industry 5.0, thereby positioning the proposed architecture as a viable solution for future smart engineering systems.
Chukwunalurum et al. (Fri,) studied this question.