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<p>In aviation, especially in autopilot systems, accuracy and efficacy play a crucial role, particularly during interruptions. This review underscores the importance of mathematical modeling as a foundational tool for designing effective control structures. Accurate modeling enables the prediction of system behavior and supports the implementation of various control strategies to meet performance and stability objectives. The study highlights the need to enhance stability, reduce the pilot’s workload, and enable complex maneuvers in both civil and military operations. It provides a comparative study of various control strategies, encompassing classical control, adaptive control, model predictive control (MPC), and robust control. The implementation of fractional-order proportional–integral–derivative controllers enhances traditional PID approaches by improving robustness against gain and phase fluctuations, thus reducing overshoot, settling time, and oscillations. Advanced methodologies such as fuzzy model reference learning control and hybrid architectures improves system stability and responsiveness in the presence of nonlinearity and disturbances. The review also explores the recent development in adaptive control using closed-loop reference models to address high oscillation issues. MPC proves particularly beneficial for large-scale systems due to its ability to handle various performance variables while regulating internal dynamics and external disturbances. Sample-efficient probabilistic model predictive control further refines MPC by continuously modifying control sequences to mitigate disturbances. The integration of neural networks and AI-based control strategies enhances noise handling while reducing computation complexities, pointing toward more intelligent and adaptable autopilot solutions.</p>

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A Survey of Autopilot Control Systems: From Classical PID to Intelligent Adaptive Controllers

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • M. Mubeena,
  • S. Mullai Venthan,
  • M. S. Nisha,
  • P. Senthil Kumar,
  • P. Vijayakumar,
  • Gayathri Rangasamy

摘要

In aviation, especially in autopilot systems, accuracy and efficacy play a crucial role, particularly during interruptions. This review underscores the importance of mathematical modeling as a foundational tool for designing effective control structures. Accurate modeling enables the prediction of system behavior and supports the implementation of various control strategies to meet performance and stability objectives. The study highlights the need to enhance stability, reduce the pilot’s workload, and enable complex maneuvers in both civil and military operations. It provides a comparative study of various control strategies, encompassing classical control, adaptive control, model predictive control (MPC), and robust control. The implementation of fractional-order proportional–integral–derivative controllers enhances traditional PID approaches by improving robustness against gain and phase fluctuations, thus reducing overshoot, settling time, and oscillations. Advanced methodologies such as fuzzy model reference learning control and hybrid architectures improves system stability and responsiveness in the presence of nonlinearity and disturbances. The review also explores the recent development in adaptive control using closed-loop reference models to address high oscillation issues. MPC proves particularly beneficial for large-scale systems due to its ability to handle various performance variables while regulating internal dynamics and external disturbances. Sample-efficient probabilistic model predictive control further refines MPC by continuously modifying control sequences to mitigate disturbances. The integration of neural networks and AI-based control strategies enhances noise handling while reducing computation complexities, pointing toward more intelligent and adaptable autopilot solutions.