AUTOMATIC TRACKING AND CONTROL SYSTEM

Abstract

Smart systems increasingly require advanced tracking and control strategies to ensure high performance, robustness, adaptability, and energy efficiency in dynamic operating environments. This paper presents an automatic tracking and control framework based on a modular Cloud–IoT architecture integrating data acquisition, state estimation, decision making, and control subsystems. The proposed approach employs a nonlinear state-space model to represent system dynamics and formulates trajectory tracking as an optimization problem that minimizes the error between the actual and reference trajectories.

To improve performance under disturbances and uncertainties, the framework incorporates state estimation techniques and adaptive decision mechanisms. A Nonlinear Model Predictive Control (NMPC) strategy is adopted to optimize tracking accuracy while limiting control effort and energy consumption. The optimization process is formulated through a performance index that balances precision, robustness, and efficiency.

The proposed architecture is validated through simulation using nonlinear dynamic models and discrete-event system techniques. The obtained results demonstrate accurate trajectory tracking, rapid error convergence, stable closed-loop operation, and efficient energy utilization. The Root Mean Square (RMS) tracking error is approximately 0.03, confirming the effectiveness of the proposed method. The developed framework provides a scalable and flexible solution for smart systems and establishes a foundation for future implementations incorporating artificial intelligence and intelligent control techniques.