基于Hopfield网络的柔性两轮自平衡机器人控制  

Flexible Two-wheeled Self-balancing Robot Control Based on Hopfield Neural Network

作　　者：阮晓钢[1] 赵建伟[1] 

机构地区：[1]北京工业大学电子信息与控制工程学院,北京100124

出　　处：《机器人》2010年第3期405-413,共9页Robot

基　　金：国家863计划资助项目(2007AA04Z226);国家自然科学基金资助项目(60774077);北京市人才强教计划项目;高等学校博士学科点专项科研基金资助项目(63002011200601)

摘　　要：用弹簧模仿人的腰椎,采用LQR成功实现了机器人实物控制.针对柔性两轮自平衡机器人的姿态控制,提出了一种基于联想学习的离散Hopfield网络实现方法,以生物学习控制方式实现柔性两轮自平衡机器人在姿态控制上的自适应、自组织能力.针对非线性、强耦合的柔性机器人系统,首先定义了合理的能量变化函数,并运用柔性机器人动力学方程设计了满足该动态过程的Hopfield网络控制器,然后分析了该控制器的收敛性.仿真实验表明了该方法的有效性和系统的稳定性.对实验结果进行详细分析,表明了系统姿态控制器设计的合理性和有效性.Real control of robot is realized successfully by using spring to imitate the lumbar of human-being and adopting LQR （linear quadratic regulator）. For posture control of the flexible two-wheeled self-balancing robot, a kind of discrete Hopfield network way based on associative study is presented, and self-adaptive and self-organizing posture control is accom- plished in a manner of biological control. Firstly, a reasonable energy function is defined for the nonlinear, strong coupling and flexible robot system. Then, a Hopfield neural network controller suitable for this dynamic process is designed based on the dynamics equation of the flexible robot, and its convergence is analyzed. Simulation experiments show the effectiveness of the method and prove the stability of the system. The rationality and availability of the system posture controller design are indicated through analyzing the experiment results in detail.

关 键 词：柔性机器人 HOPFIELD网络 腰椎 

分 类 号：TP24[自动化与计算机技术—检测技术与自动化装置]