Tracking control of ball and plate system using a improved PSO on-line training PID neural network

Han, Kyongwon; Tian, Yantao; Kong, Yongsu; Li, Jinsong; Zhang, Yinghui

doi:10.1109/icma.2012.6285702

Cited by 11 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreno-Armend´ariz et al implemented a fuzzy controller on FPGA based hardware to control ball and plate system in 2010 [17]. In 2011, Dong et al used PSO based neuro-fuzzy controller [18], and one year later Han et al also applied PSO to tune the PID controller parameters for ball and plate system [19]. Mochizuki et al proposed a design method of PID controller based on the generalized Kalman-Yakubovich-Popov lemma in 2013 [20].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Evolutionary Computation Based Gain Scheduling Control for Ball and Plate Stabilization System

Gözde

2019

Balkan Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

The platform stabilization systems used in marine, airborne or land vehicle applications are controlled with very different control methods basically including linear, nonlinear and artificial intelligence-based design techniques. Nowadays, evolutionary computation based optimization algorithms also provide new opportunities to engineers in order to design a gain scheduling controller. In this study, an evolutionary computation based gain scheduling controller is proposed for a ball and plate system so as to examine its control performances on a stabilization system. For this purpose, the swarm intelligence based algorithms are chosen due to their better performance than the other evolutionary computation algorithms. The results are comparatively investigated by using time domain and frequency domain analysis methods. Additionally, the robustness analysis is also applied to examine the tuning performances of these controllers in case of changing system parameters in the range of ±50%.

show abstract