The balance control of two-wheeled robot based on bionic learning algorithm

2014 ◽  
Author(s):  
Ren Hongge ◽  
Wang Zhilong ◽  
Li Fujin ◽  
Huo Meijie
Keyword(s):  
Learning Algorithm ◽  
Balance Control ◽  
Wheeled Robot

Advanced Proportional-Integral-Derivative Control Compensation Based on a Grey Estimated Model in Dynamic Balance of Single-Wheeled Robot

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 326
Author(s):  
Mao-Lin Chen ◽  
Chun-Yen Chen ◽  
Chien-Hung Wen ◽  
Pin-Hao Liao ◽  
Kai-Jung Chen
Keyword(s):  
Mathematical Modeling ◽  
Control Method ◽  
Reference Model ◽  
Balance Control ◽  
Lagrange Equation ◽  
Linear Quadratic Regulator ◽  
Dynamic State ◽  
Linear Quadratic ◽  
Wheeled Robot ◽  
The One

This paper aims to design a one-wheeled robot as regards its pitch freedom and balance control on the one hand and to assess the application feasibility of the GM (1,1) swing estimation controller on the other. System control focuses mainly on one-wheeled robot stability, body swings in position, and speed control. Mathematical modeling and GM (1,1) prediction control are under investigation. The mathematical modeling is firstly conducted through referencing to the Newtonian mechanics and the Lagrange equation, from which the robot transfer function and state-space differential equation are derived. Next, the linear quadratic regulator is applied as the control rule at the balance point. Applying GM (1,1) to assess the robot gyro signal at a dynamic state is a discussion. Next, model reference estimation control is processed, and a mathematical model of the balance control method is completed. Finally, a simulation is conducted to verify the feasibility of the GM (1,1) estimation reference model. The linear quadratic regulator, which is credited with tenacity, can provide pitch swing and balance control of the one-wheeled robot.

Model Learning for Two-Wheeled Robot Self-Balance Control

2019 ◽  
Author(s):  
Enbo Li ◽  
Haibo Feng ◽  
Haitao Zhou ◽  
Xu Li ◽  
Yanwu Zhai ◽  
...  
Keyword(s):  
Balance Control ◽  
Model Learning ◽  
Wheeled Robot

scholarly journals Design and Development of Leg-Wheel Hybrid Hexapod along with Machine Learning Algorithm implementation

2015 ◽  
Vol 4 (4) ◽  
pp. 262
Author(s):  
Harikrishnan Madhusudanan
Keyword(s):  
Concurrent Engineering ◽  
Learning Algorithm ◽  
Legged Robot ◽  
Robotic Platform ◽  
Design And Development ◽  
Design Metrics ◽  
Advantages And Disadvantages ◽  
Wheeled Robot ◽  
Primary Focus ◽  
Algorithm Implementation

The conventional mobile robotic platforms which either uses wheels or legs are quite familiar and each one of them has its own advantages and disadvantages. The wheeled robot is suitable for only plain and smooth terrain, whereas the legged robot can travel in any kind of terrain but is comparatively slower than the wheeled robot. So, a hybrid of both wheeled and legged platform would be quite suitable for any kind of terrain. The primary focus of this paper is to design and develop a leg-wheel hybrid robotic platform with a concurrent engineering and mechatronics approach to produce results with optimised design metrics at each and every stage of its development. An overall view of the entire mechatronics system is considered for design and development of the robot at each and every stage rather than a sequential engineering approach.

The Study of Artificial Endocrine Network Model and its Application in Robot Navigation Control

2011 ◽  
Vol 341-342 ◽  
pp. 685-689
Author(s):  
Guo Rui Huang ◽  
Yan Mou Zhan ◽  
Xu Cheng ◽  
Hua Qiang Wu ◽  
Hao Li
Keyword(s):  
Network Model ◽  
Simulation Experiment ◽  
Learning Algorithm ◽  
Balance Control ◽  
Dynamic Balance ◽  
Endocrine System ◽  
Reaction Diffusion ◽  
Internal Environment ◽  
Robotic Navigation ◽  
Navigation Control

Relative stability of the internal environment is the basis for the body’s all intelligent activities, and the endocrine system plays an irreplaceable role in maintaining that stability. Based on the self-organization mechanism of the hormone reaction diffusion in the endocrine system, this paper presents the artificial endocrine network model and the model-based learning algorithm. The model depends on the diffusion of artificial hormones and its reaction with suitable receptors to achieve the dynamic balance control of the artificial endocrine network. In order to validate the feasibility of the model and algorithm, this paper makes a simulation experiment of robotic navigation control, whose results also show that the model and its algorithm has good adaptive solving ability.

Sign in / Sign up

Export Citation Format

Share Document