摘要
针对本质上非线性、强耦合的两轮自平衡机器人复杂动态系统,构造操作条件反射概率自动机(OCPA)仿生自主学习系统.OCPA仿生自主学习系统是一个基于Skinner操作条件反射的概率自动机,主要特征在于模拟生物的操作条件反射机制,具有仿生的自组织功能,包括自学习和自适应功能,可用于描述、模拟、设计各种自组织系统.从理论上分析OCPA学习系统的操作条件反射学习机制的收敛性.应用于两轮机器人姿态平衡控制的仿真和实验结果均表明,设计的OCPA仿生自主学习系统不需要系统的模型,通过模拟生物的操作条件反射机制,自组织地渐进形成、发展和完善其姿态平衡控制技能.
An operant conditioning probabilistic automation （OCPA） bionic autonomous learning system is constructed according to nonlinear, strong-coupling and complex two-wheeled self-balancing robot dynamic system. The OCPA bionic autonomous learning system is a probabilistic automaton based on Skinner operant conditioning whose main character lies in simulating the operant conditioning mechanism of biology. And it has bionic self-organization function which contains the self-learning and adaptive functions, and thus the OCPA automaton can be used to describe, simulate and design various self-organization systems. The convergence of operant conditioning learning algorithm of OCPA learning system is proved theoretically. The results of both simulation and experiment applied to two-wheeled robot poster balance control indicate that the OCPA learning system does not require the robot model, and the motion balanced skills of robot are formed, developed and perfected gradually by simulating the operant conditioning mechanism of biology.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2011年第1期138-146,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60774077)
国家863计划项目(No.2007AA04Z226)
北京市教委重点项目(No.KZ200810005002)资助
