摘要
针对滑模控制在机械臂的应用领域中出现的收敛速度较慢和系统抖振问题,以提高机械臂动态特性为目标,设计一种改进变结构趋近律,利用分组特点与反双曲正弦函数的特性优化收敛速度,同时对系统的抖振作出有效的抑制。利用第二类拉格朗日方程建立二自由度机械臂的数学模型,且针对系统中存在的摩擦和其他不可测干扰问题,以RBF神经网络对系统模型进行逼近。基于Lyapunov函数证明系统跟踪的稳定性。最后在Simulink中与PID、等速趋近律和快速幂次趋近律等方法进行实验对比,验证改进变结构趋近律算法的可行性和稳定性。
Aiming at the problems of slow convergence speed and system chattering in the application field of sliding mode control in the manipulator,an improved variable structure reaching law is designed to improve the dynamic characteristics of the manipulator.The convergence speed and chattering suppression of the system are optimized by using the grouping characteristics and the inverse hyperbolic sine function.The mathematical model of the two degree of freedom manipulator is established by using the second Lagrange equation,and the RBF neural network is used to approximate the system model for the friction and other unmeasurable interference problems in the system.The stability of the system tracking is proved by Lyapunov function method.Finally,the feasibility and stability of the improved variable structure reaching law algorithm are verified by the experimental comparison with PID,constant rate reaching law and fast power reaching law in Simulink.
作者
宋涛涛
李艳萍
李洪港
韩春雪
SONG Tao-tao;LI Yan-ping;LI Hong-gang;HAN Chun-xue(School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,China)
出处
《计算机与现代化》
2023年第12期14-18,共5页
Computer and Modernization
基金
国家自然科学基金资助项目(62133008)。
关键词
机械臂
滑模控制
改进变结构趋近律
RBF神经网络
抖振
manipulator
sliding mode control
improved variable structure approach law
RBF neural network
chattering
