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Open and closed-loop control systems01:17

Open and closed-loop control systems

912
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
912
PD Controller: Design01:26

PD Controller: Design

318
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,...
318
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

139
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.
In the absence...
139
State Space Representation01:27

State Space Representation

264
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
264
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

166
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...
166
PID Controller01:19

PID Controller

202
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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Related Experiment Video

Updated: Aug 22, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM.

Muhammad Usama1, In-Young Lee2

  • 1Department of Electrical Engineering, Chosun University, 309, Pilmun-daero, Dong-gu, Gwangju 61452, Korea.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep symbolic regression (DSR) current controller for surface-mounted permanent magnet synchronous machines (SPMSMs). The DSR controller outperforms traditional methods, offering better adaptability and potential for future power electronics applications.

Keywords:
closed-loop controldata fitting expressiondeep learningdeep symbolic optimizationmetaheuristic algorithmsymbolic regression

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Traditional proportional-integral (PI) current controllers for SPMSMs are highly dependent on system models.
  • Developing robust controllers that perform well under varying conditions is crucial for power electronics applications.

Purpose of the Study:

  • To design a novel current controller for SPMSMs using deep symbolic regression (DSR).
  • To overcome limitations of traditional PI controllers by developing a model-agnostic and adaptable control scheme.
  • To tune outer speed control loop gains using an optimal cuckoo search algorithm.

Main Methods:

  • Employing deep symbolic regression (DSR) to generate an analytical dynamic numerical expression for current control.
  • Training and fitting the DSR model to characterize machine data.
  • Tuning speed control loop gains with the cuckoo search algorithm.
  • Comparing the DSR-based controller against traditional vector control under various operating conditions.

Main Results:

  • The DSR-based controller demonstrated superior performance compared to traditional vector control.
  • The proposed controller showed excellent extrapolation capabilities beyond the training dataset.
  • Optimal gain values for the speed control loop were achieved using the cuckoo search algorithm.

Conclusions:

  • Deep symbolic regression offers a powerful, adaptable approach for current control in SPMSMs.
  • The DSR-based controller provides a foundation for advanced deep learning techniques in power conversion.
  • This method overcomes the model dependency issues of traditional PI controllers.