Control of a two-wheeled self-balancing robot with support vector regression method

Abstract

Recently, learning based control is a popular topic on robotic applications. This paper presents a novel learning based intelligent control method which realizes the balance control of a statically unstable and dynamically stable robot-a two-wheeled self-balancing robot. The control strategy could be segmented into two levels: a learning based controller using Support Vector Regression approach as a high level and a traditional PD controller as a low level. Support Vector Regression is utilized to learn the mapping between robot's state data and corresponding actions from experiments by using the inclined angle and its angular speed as inputs and the wheels velocity of the robot needed to keep balance as outputs. And the low level PD controller makes sure the motors achieve the velocity value gained before. Experiments are taken to show that the control method is useful and efficient. Additionally, this paper presents a practice of learning based control.

Modeling and Design of a New Type of Self-Balancing Obstacle Vehicle

“…Assume that the body is out of balance tilted forward, than we have to have motors drives the wheels to have an acceleration to keep the body in balance, so the system can maintain the equilibrium state. Due to the complexity of the system, the non-stability and the nonlinearity of the system, the problems such as the following problems, such as the following problems, such as the following, the tracking and the robustness of the system [13][14] . Using Newton method for mechanical analysis, the main parameters that affect the balance car: the weight of the body, the radius of the wheel, the center of gravity .In order to maintain the dynamic balance of the system, the center of gravity of the body should be in a certain angle range of the center line of the vehicle.…”

Use of PID control during Education in Reinforcement Learning on Two Wheel Balance Robot

“…This software is fed online by sensors and transducers [9] with the information they receive from the outside environment. Researchers have proposed several control approaches: Liangliang Cui horse et al worked on the method of Support Vector Regression [10], Chia-Hong Chen et al suggested Fuzzy Logic [2], and [4] Proportional Integral Derivative (PID), Linear Quadratic Regulator (LQR) and pole placement control methods were investigated. Ebin Philip, Sharath Golluri investigated cascade PID control systems for autonomous balanced driving robots [15].…”