| Abstract: |
Dynamics and control form the backbone of modern engineering systems, enabling the design, analysis, and operation of complex, interconnected, and autonomous systems. This review presents a comprehensive analysis of the evolution of control methodologies and dynamic modeling techniques from 2000 to 2025. Early studies focused on classical control approaches, including PID, state-space, and robust control, while mid-2010s research emphasized predictive and networked control systems for distributed applications. In recent years, data-driven, intelligent, and hybrid control strategies have emerged, integrating machine learning and adaptive methods with classical frameworks to address nonlinearities, uncertainties, and high-dimensional dynamics. The paper highlights applications across robotics, autonomous vehicles, industrial automation, and smart transportation systems. Key research gaps are identified, including the integration of model-based and data-driven methods, scalability of networked systems, real-time implementation, robustness under uncertainty, and the lack of standardized benchmarking. This review provides insights into trends, challenges, and future directions, guiding researchers toward the development of intelligent, scalable, and resilient control systems for complex engineering applications. |
