Open AccessLQR/Sliding Mode Controller Design Using Particle Swarm Optimization for Crane System
Author(s) -
Hazem I. Ali,
Azhar Jabbar Abdulridha,
Rawaa Khaleel,
Kareem Kareem A. Hussein
Publication year - 2020
Publication title -
mağallaẗ al-nahrayn li-l-ʿulūm al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2521-9162
pISSN - 2521-9154
DOI - 10.29194/njes.23010045
Subject(s) - control theory (sociology) , linear quadratic regulator , particle swarm optimization , robustness (evolution) , sliding mode control , inverted pendulum , controller (irrigation) , nonlinear system , robust control , control engineering , engineering , computer science , optimal control , control system , mathematics , control (management) , mathematical optimization , biochemistry , chemistry , physics , electrical engineering , quantum mechanics , artificial intelligence , machine learning , biology , agronomy , gene
In this work, the design procedure of a hybrid robust controller for crane system is presented. The proposed hybrid controller combines the linear quadratic regulator (LQR) properties with the sliding mode control (SMC) to obtain an optimal and robust LQR/SMC controller. The crane system which is represented by pendulum and cart is used to verify the effectiveness of the proposed controller. The crane system is considered one of the highly nonlinear and uncertain systems in addition to the under-actuating properties. The parameters of the proposed LQR/SMC are selected using Particle Swarm Optimization (PSO) method. The results show that the proposed LQR/SMC controller can achieve a better performance if only SMC controller is used. The robustness of the proposed controller is examined by considering a variation in system parameters with applying an external disturbance input. Finally, the superiority of the proposed LQR/SMC controller over the SMC controller is shown in this work.