Controlling real self-balancing bicycle model using genetic algorithm for optimizing LQR controller
Corressponding author's email:
1881101@student.hcmute.edu.vnKeywords:
bikes, motorbikes, control balance, LQR controller, genetic algorithm, LQR-GA controllerAbstract
It is difficult to control balance vehicles such as bikes or motorbikes when you are a beginner. In order to control balance for the above a vehicle, a human must hold vehicles move a variable step, or know as control velocity. Nowadays, automatic engineering is advanced more and more, engineers can control balance bikes or motorbikes when they are not moving. In this paper, the authors choose a model which is a self-balancing bike controlled by a wheel. Then, the research results about GA application for optimizing Linear Quadratic Algorithm (LQR) are published. LQR Controller can control balance for bikes or motorbikes. However, in order to get high quality, the authors suggest optimizing traditional LQR controller. Authors use GA to search Q, with Q is searched, Riccati equation is optimized. LQR-GA controller can apply into self-driving vehicles, self-balancing bicycle for training children and future vehicles. Results of LQR-GA are verified better through generations on Matlab/Simulink simulation and real model.
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