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PI Controller: Design01:24

PI Controller: Design

321
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...
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Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

136
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...
136
PID Controller01:19

PID Controller

136
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...
136
PD Controller: Design01:26

PD Controller: Design

272
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,...
272
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

153
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...
153
Electro-mechanical Systems01:19

Electro-mechanical Systems

1.0K
Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...
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Updated: Jul 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor.

Ulbio Alejandro-Sanjines1, Anthony Maisincho-Jivaja1, Victor Asanza2

  • 1Escuela Superior Politécnica del Litoral, Guayaquil 090903, Ecuador.

Biomimetics (Basel, Switzerland)
|September 27, 2023
PubMed
Summary

This study introduces an adaptive PID controller for DC motor speed control using reinforcement learning. The novel approach optimizes controller gains automatically, improving efficiency in automated industrial processes.

Keywords:
DDPG TD3adaptive PIartificial intelligenceneural networkreinforcement learning

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Robotics

Background:

  • Automated industrial processes rely on controllers for precise output signal generation.
  • Classical PID controllers require retuning when system conditions change, impacting production.
  • Existing control methods face challenges with dynamic or changing system parameters.

Purpose of the Study:

  • To develop an adaptive PID controller for DC motor speed control.
  • To utilize artificial intelligence, specifically reinforcement learning, for autonomous controller tuning.
  • To eliminate the need for prior system knowledge in controller gain determination.

Main Methods:

  • Implemented an adaptive PID controller for a DC motor speed plant.
  • Employed a reinforcement learning algorithm with an actor-critic agent.
  • Utilized the Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) algorithm.
  • Configured a neural network with 300 neurons for agent learning.

Main Results:

  • The adaptive PID controller successfully generated appropriate gains without system identification.
  • The DDPG TD3 algorithm facilitated policy optimization and critic training.
  • Performance comparison demonstrated the effectiveness of the developed controller against classical methods.

Conclusions:

  • The developed adaptive PID controller offers a robust solution for DC motor speed control in dynamic environments.
  • Reinforcement learning, particularly DDPG TD3, provides an effective mechanism for automated controller tuning.
  • This AI-driven approach enhances industrial process efficiency by eliminating manual retuning requirements.