Advanced Control Strategies for Autonomous Engineering Systems

This research evaluates and organizes advanced control methods for autonomous engineering systems that must operate in changing and uncertain environments. Conventional linear controllers—like proportional‑integral‑derivative (PID) controllers—often lose performance when the system shows nonlinear behavior, when parameters shift, or when external disturbances act on it. To overcome these issues, the study reviews and groups several modern approaches: robust control, adaptive control, model‑predictive control, sliding‑mode control, and hybrid intelligent control. It places special focus on the mathematical models and stability conditions of each method when they are used for unmanned aerial vehicles (UAVs), robotic manipulators, and intelligent transportation systems. The framework also tackles key practical problems, such as sensor noise, communication delays,

limited computing power, and environmental disturbances. By comparing transient response, steady‑state tracking accuracy, and the ability to reject disturbances, the work provides a solid basis for choosing and designing the best control laws. The resulting documentation and modeling guidelines give a clear path for building high‑fidelity simulations and testing controller robustness in complex autonomous applications.

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