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Related Concept Videos

PID Controller01:19

PID Controller

100
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...
100
PI Controller: Design01:24

PI Controller: Design

199
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
199
PD Controller: Design01:26

PD Controller: Design

183
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
183
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

83
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
83
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

104
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
104
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

92
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
92

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Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
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The Application and Optimisation of a Neural Network PID Controller for Trajectory Tracking Using UAVs.

Michał Siwek1, Leszek Baranowski1, Edyta Ładyżyńska-Kozdraś2

  • 1Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, Kaliskiego 2 Street, 00-908 Warsaw, Poland.

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|January 8, 2025
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Summary

This study introduces a novel neural network-enhanced pitch control system for unmanned aerial vehicles (UAVs) flying near and above supersonic speeds. The optimized controller significantly reduces flight height errors, improving UAV maneuverability.

Keywords:
PID tuningUAVZiegler–Nichols II methodneural networkpath trackingpitch channel controltrajectory tracking

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Area of Science:

  • Aerospace Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Unmanned aerial vehicles (UAVs) face control challenges when operating at high speeds, especially near the speed of sound.
  • Existing control systems may struggle with rapid trajectory changes, particularly in altitude.
  • Accurate trajectory following is critical for mission success and safety.

Purpose of the Study:

  • To develop and evaluate a novel pitch channel control system for UAVs operating at high subsonic and supersonic speeds.
  • To improve the precision of UAV altitude control during dynamic flight maneuvers.
  • To investigate the application of neural networks for optimizing proportional-integral-differential (PID) controller gains.

Main Methods:

  • A proportional-integral-differential (PID) controller was designed for UAV pitch control.
  • Initial PID controller gains were determined using the Ziegler-Nichols II method.
  • A recurrent back-propagation neural network (PIDNN) was employed to optimize PID gains, minimizing height error.
  • Simulations were conducted for various flight conditions, including subsonic, transonic, and supersonic speeds at different altitudes.

Main Results:

  • The PIDNN-optimized controller demonstrated a significant reduction in height errors compared to the initial PID controller.
  • The optimized controller exhibited enhanced flexibility in managing rapid altitude changes.
  • Simulations confirmed the effectiveness of the neural network approach across a range of flight speeds and altitudes.
  • The novel PIDNN method for gain determination is a key contribution.

Conclusions:

  • Optimizing PID controller gains with a recurrent neural network (PIDNN) substantially improves UAV altitude control accuracy.
  • The proposed control system enhances UAV performance and maneuverability at high speeds.
  • This approach offers a promising method for adaptive and robust UAV flight control.