Bionic autonomous learning control of a two-wheeled self-balancing flexible robot |
| |
Authors: | Jianxian CAI and Xiaogang RUAN |
| |
Affiliation: | 1. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;Institute of Disaster Prevention, Sanhe Hebei 065201, China 2. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China |
| |
Abstract: | This paper presents an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner’s
operant conditioning theory for solving the balance control problem of a two-wheeled flexible robot. The OCPA learning system
consists of two stages: in the first stage, an operant action is selected stochastically from a set of operant actions and
then used as the input of the control system; in the second stage, the learning system gathers the orientation information
of the system and uses it for optimization until achieves control target. At the same time, the size of the operant action
set can be automatically reduced during the learning process for avoiding little probability event. Theory analysis is made
for the designed OCPA learning system in the paper, which theoretically proves the convergence of operant conditioning learning
mechanism in OCPA learning system, namely the operant action entropy will converge to minimum with the learning process. And
then OCPA learning system is applied to posture balanced control of two-wheeled flexible self-balanced robots. Robot does
not have posutre balanced skill in initial state and the selecting probability of each operant in operant sets is equal. With
the learning proceeding, the selected probabilities of optimal operant gradually tend to one and the operant action entropy
gradually tends to minimum, and so robot gradually learned the posture balanced skill. |
| |
Keywords: | Two-wheeled flexible robot Poster balance control Operant conditioning Probabilistic automaton Bionic autonomous learning |
本文献已被 维普 万方数据 SpringerLink 等数据库收录! |
| 点击此处可从《控制理论与应用(英文版)》浏览原始摘要信息 |
|
点击此处可从《控制理论与应用(英文版)》下载全文 |
|