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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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Gradient-based adaptive PID-SMC control tuned by ant colony optimization for autonomous underwater vehicle.

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An adaptive PID-based sliding mode controller (APID-SMC) enhances autonomous underwater vehicle (AUV) trajectory tracking. Optimized with ant colony optimization (ACO), it offers superior accuracy and robustness in challenging marine environments.

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Adaptive PID controlAutonomous underwater vehicleGradient descent algorithmSliding mode controlTrajectory tracking

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Area of Science:

  • Robotics and Control Systems
  • Marine Engineering
  • Artificial Intelligence

Background:

  • Autonomous Underwater Vehicles (AUVs) require robust control for accurate trajectory tracking.
  • Conventional controllers like Sliding Mode Control (SMC) and PID often struggle with external disturbances and uncertainties.
  • Enhancing AUV control performance is crucial for complex marine operations.

Purpose of the Study:

  • To propose and evaluate an Adaptive PID-based Sliding Mode Controller (APID-SMC) for AUVs.
  • To optimize the APID-SMC using Ant Colony Optimization (ACO) for improved trajectory tracking.
  • To demonstrate the controller's enhanced robustness against external disturbances and uncertainties.

Main Methods:

  • Development of an APID-SMC integrating SMC robustness with PID smooth control.
  • Online tuning of PID gains using a Gradient Descent Algorithm (GDA).
  • Optimization of learning rates and sliding surface coefficients via ACO to minimize trajectory-tracking error.

Main Results:

  • APID-SMC significantly reduced Integral Absolute Error (IAE) by up to 27.97% and Integral Time Absolute Error (ITAE) by up to 82.84% compared to conventional methods.
  • The controller exhibited superior transient performance and reduced control signal chattering.
  • Demonstrated stability and effectiveness under extreme noise and uncertainties in simulated marine environments.

Conclusions:

  • The proposed APID-SMC offers a highly effective and practical control solution for AUVs.
  • ACO-based optimization enhances convergence speed and performance consistency.
  • APID-SMC provides improved trajectory-tracking accuracy and robustness in complex marine settings.