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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.
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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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Adaptive neural PD controllers for mobile manipulator trajectory tracking.

Jesus Hernandez-Barragan¹, Jorge D Rios¹, Javier Gomez-Avila¹

¹Department of Computer Science, University of Guadalajara, Guadalajara, Jalisco, México.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

This study introduces adaptive neural controllers for mobile manipulators, improving position tracking by eliminating steady-state errors and oscillations. These advanced controllers offer faster learning and better performance than traditional methods.

Keywords:

Adaptive PID Mobile manipulator Neural control PID

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent enhancements of standard industrial controllers.
Traditional PID controllers can cause oscillations, overshoot, and windup effects due to the integral term.
Mobile manipulators combine mobility and manipulation, offering significant industrial application potential.

Purpose of the Study:

To present adaptive neuron PD and multilayer neural PD controllers for mobile manipulator position tracking.
To demonstrate the effectiveness of Extended Kalman Filter (EKF) training for neural network controllers.
To show the advantages of dynamic gain adjustment in eliminating steady-state errors and reducing oscillations.

Main Methods:

Development of an adaptive neuron PD controller and a multilayer neural PD controller.
Training of neural network controllers using the Extended Kalman Filter (EKF) algorithm.
Simulation and comparison with conventional PID and existing adaptive neuron PID controllers on a KUKA Youbot mobile manipulator.

Main Results:

EKF-trained neural networks exhibit faster learning speeds and convergence compared to backpropagation.
The proposed neural PD controllers dynamically adjust gains, eliminating steady-state error without an integral term.
Oscillations, overshoot, and windup effects are significantly reduced, improving system performance and stability.

Conclusions:

Adaptive neural PD controllers offer a superior alternative to conventional PID controllers for mobile manipulator position tracking.
Eliminating the integral term simplifies control design and enhances robustness by avoiding windup issues.
The proposed controllers demonstrate practical applicability and improved performance in industrial robotic systems.