Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

Electronics

Javier Gomez-Avila¹  AlmaY. Alanis¹  Carlos Lopez-Franco¹  JorgeD. Rios¹  Jesus Hernandez-Barragan¹  Nancy Arana-Daniel¹ 

[1] Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. Marcelino García Barragán 1421, Guadalajara C.P. 44430, Jalisco, Mexico;

关键词: neuron PID;    Kalman filtering;    omnidirectional mobile robot;    implementations;    anti-windup;   

DOI  :  10.3390/electronics9040636

来源: DOAJ

【 摘 要 】

In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA^® Youbot^® omnidirectional platform.

【 授权许可】

Unknown