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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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An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable

Richa Sharma1, Vikas Kumar1, Prerna Gaur2

  • 1Department of Electrical and Instrumentation Engineering, Thapar University Patiala, 147004, India.

ISA Transactions
|February 28, 2016
PubMed
Summary

This study introduces a novel Mix Locally Recurrent Neural Network based PID (MLRNNPID) controller for robotic manipulators. The MLRNNPID controller, optimized with Cuckoo Search Algorithm, demonstrates superior performance in handling nonlinearities and uncertainties.

Keywords:
Artificial neural networksCuckoo search algorithmOn-line learningRecurrent neural networksRobotic manipulator

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Robotic manipulators are complex, non-linear systems challenging for conventional PID controllers.
  • Tuning PID controller gains and adapting to parameter variations are crucial for effective control.

Purpose of the Study:

  • To develop an advanced PID controller using a Mix Locally Recurrent Neural Network (MLRNN) architecture.
  • To enhance robotic manipulator control by improving PID controller adaptability and robustness.

Main Methods:

  • Implemented an MLRNN architecture to mimic PID controller functions (proportional, integral, derivative nodes).
  • Utilized a Cuckoo Search Algorithm (CSA) for initializing MLRNNPID controller gains.
  • Employed a sequential learning least squares algorithm for on-line gain adaptation.
  • Analyzed stability using Lyapunov stability criteria.

Main Results:

  • The MLRNNPID controller effectively managed parameter uncertainties and external disturbances in a two-link robotic manipulator with variable payload.
  • Demonstrated superior performance compared to CSA optimized Neural Network PID (OPTNNPID) and conventional PID (OPTPID) controllers.
  • Validated controller stability through Lyapunov criteria.

Conclusions:

  • The proposed MLRNNPID controller offers a robust and adaptive solution for controlling complex robotic manipulators.
  • This approach significantly enhances control effectiveness over conventional and other optimized PID methods in uncertain environments.