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Improve Linear Quadratic Regulator by Particle Swarm Optimization Algorithms for Two Wheeled Self Balancing Mobile robot
Author(s) -
Ekhlas H. Karam,
Noor Mjeed
Publication year - 2017
Publication title -
iraqi journal for electrical and electronic engineering
Language(s) - English
Resource type - Journals
eISSN - 2078-6069
pISSN - 1814-5892
DOI - 10.37917/ijeee.13.2.4
Subject(s) - linear quadratic regulator , particle swarm optimization , control theory (sociology) , multivariable calculus , stability (learning theory) , riccati equation , computer science , computation , mathematical optimization , robot , optimal control , mathematics , algorithm , control engineering , control (management) , engineering , artificial intelligence , mathematical analysis , machine learning , differential equation
The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time. Kywords —ASO, LQR, PSO, two wheeled self-balancing mobile robot.

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