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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex

Zirong Wang1, Zhengyu Han1, Shahzadi Tayyaba2

  • 1Equipment Management and Unmanned Aerial Vehicle Engineering School, Air Force Engineering University, Xi'an, Shaanxi, China.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary

This study introduces an ATT-Bi-LSTM framework to enhance uncrewed aerial vehicle (UAV) control by integrating communication signals for adaptive parameter adjustment. The novel approach significantly improves attitude estimation accuracy and optimizes UAV positioning.

Keywords:
Attention mechanismAttitude estimationBI-LSTMLSTMUAV control

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Uncrewed Aerial Vehicles (UAVs) are increasingly used in aviation, military, and logistics for efficiency and safety.
  • Real-time parameter adjustments are crucial for UAV flight safety and efficacy in complex environments.
  • Accurate attitude estimation is vital for effective UAV operation and control.

Purpose of the Study:

  • To develop an advanced framework for optimizing UAVs through adaptive parameter control.
  • To enhance attitude estimation accuracy by integrating communication signal data.
  • To improve the real-time control and positioning of UAVs.

Main Methods:

  • Introduction of the ATT-Bi-LSTM framework for UAV optimization.
  • Utilizing a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) for enhanced feature extraction.
  • Employing an attention mechanism to amplify LSTM network output for optimal positioning control.
  • Integrating state information from communication signals for adaptive parameter adjustment.

Main Results:

  • The ATT-Bi-LSTM framework demonstrated commendable performance in empirical validation using optical system data.
  • The model achieved superior accuracy in estimating critical attitude indicators: yaw, pitch, and roll.
  • The proposed model exhibited the lowest error rates (RMSR and MAE) compared to existing methods.

Conclusions:

  • The ATT-Bi-LSTM framework offers a robust solution for enhancing UAV attitude estimation and control.
  • The study provides significant algorithmic support and a valuable reference for future UAV optimization research.
  • Adaptive parameter control integrated with communication signal data is effective for improving UAV performance.