Adaptive neural PD controllers for mobile manipulator trajectory tracking
| Autor: | Alma Y. Alanis, Carlos Lopez-Franco, Jorge D. Rios, Nancy Arana-Daniel, Javier Gomez-Avila, Jesus Hernandez-Barragan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
General Computer Science Artificial neural network Settling time Mobile manipulator Computer science PID PID controller 02 engineering and technology Robotics Neural control Backpropagation lcsh:QA75.5-76.95 Extended Kalman filter 020901 industrial engineering & automation Adaptive PID Control theory Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Overshoot (signal) 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science |
| Zdroj: | PeerJ Computer Science, Vol 7, p e393 (2021) PeerJ Computer Science |
| ISSN: | 2376-5992 |
| Popis: | Artificial intelligence techniques have been used in the industry to control complex systems; among these proposals, adaptive Proportional, Integrative, Derivative (PID) controllers are intelligent versions of the most used controller in the industry. This work presents an adaptive neuron PD controller and a multilayer neural PD controller for position tracking of a mobile manipulator. Both controllers are trained by an extended Kalman filter (EKF) algorithm. Neural networks trained with the EKF algorithm show faster learning speeds and convergence times than the training based on backpropagation. The integrative term in PID controllers eliminates the steady-state error, but it provokes oscillations and overshoot. Moreover, the cumulative error in the integral action may produce windup effects such as high settling time, poor performance, and instability. The proposed neural PD controllers adjust their gains dynamically, which eliminates the steady-state error. Then, the integrative term is not required, and oscillations and overshot are highly reduced. Removing the integral part also eliminates the need for anti-windup methodologies to deal with the windup effects. Mobile manipulators are popular due to their mobile capability combined with a dexterous manipulation capability, which gives them the potential for many industrial applications. Applicability of the proposed adaptive neural controllers is presented by simulating experimental results on a KUKA Youbot mobile manipulator, presenting different tests and comparisons with the conventional PID controller and an existing adaptive neuron PID controller. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|