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Related Concept Videos

Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

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Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
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Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

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Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
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Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

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Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
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Controller Configurations01:22

Controller Configurations

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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.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

162
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...
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Time and frequency -Domain Interpretation of PI Control01:27

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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.
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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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Deterministic learning-based neural network control with adaptive phase compensation.

Yiming Fei1, Dongyu Li2, Yanan Li3

  • 1School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive phase compensator to enhance deterministic learning-based control. The method improves neural network learning speed and accuracy for nonlinear systems, boosting overall control performance.

Keywords:
Adaptive phase compensationDeterministic learningNeural network learning controlRadial basis function neural network (RBFNN)

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Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Nonlinear Dynamics

Background:

  • Deterministic learning-based control using radial basis function neural networks (RBFNNs) can identify nonlinear system dynamics under persistent excitation (PE).
  • Current RBFNN control schemes face limitations in learning speed and accuracy due to the trade-off between PE levels and approximation capabilities.

Purpose of the Study:

  • To enhance the performance of deterministic learning-based adaptive feedforward control by improving the nonlinear approximation capability of RBFNNs.
  • To introduce an adaptive phase compensator inspired by frequency domain techniques for linear time-invariant (LTI) systems.

Main Methods:

  • An adaptive phase compensator utilizing pure time delay is proposed.
  • The phase compensator is integrated into the hidden layer of the RBFNN.
  • Theoretical stability analysis is performed for systems affine in control.

Main Results:

  • The adaptive phase compensation effectively improves the nonlinear approximation capability of the RBFNN.
  • Both learning performance (speed and accuracy) and control performance are enhanced.
  • Simulation studies validate the proposed method's effectiveness.

Conclusions:

  • The proposed adaptive phase compensation method significantly improves deterministic learning-based RBFNN control for nonlinear systems.
  • The integration of time-delay-based phase compensation offers a novel approach to overcoming RBFNN limitations.
  • The control scheme is proven stable for a class of affine systems.