摘要
为了实现受约束空间机器人的高精度控制,提出了一种基于U-K(Udwadia-Kalaba)方程的降阶自适应神经网络滑模控制算法;基于U-K方程,同时考虑受约束空间机器人各个关节的理想约束力与非理想约束力,推导得到详细的动力学方程;考虑到非理想约束力具有不确定性且单独采用滑模控制会出现抖振现象,提出了自适应神经网络滑模控制算法,实现各关节角度、角速度以及非理想约束力的高精度跟踪;针对系统受约束模型,对动力学方程和滑模控制器进行了降阶求解,减少了变量并简化了计算过程;为了验证所提算法的正确性与合理性,以2自由度受约束空间机器人为例进行了仿真验证;仿真结果表明:受约束空间机器人的各关节角度、角速度以及非理想约束力的跟踪误差均低于10^(-4)量级。
In order to achieve the high precision control of the constrained space robot,a reduced order adaptive neural network sliding mode control algorithm based on Udwadia-Kalaba(U-K)equation is proposed.On the basis of U-K equation and considering the ideal and non-ideal constrained forces of the constrained space robot,the detailed dynamic equations are derived.Considering the uncertainty of the non-ideal constrained forces,and sliding mode control alone can cause chattering phenomenon,a adaptive neural network sliding mode control algorithm is proposed to realize the high-precision tracking of each joint angle,angle speed and non-ideal constrained force.For the constrained model of the system,the dynamic equation and sliding mode controller are reduced the order to decrease the variables and simplify the calculation process.To verify the correctness and rationality of the proposed algorithm,the simulation and verification are carried out using 2-DOF constrained space robot as an example.The simulation results show that the tracking errors of the joint angle,angle speed and non-ideal constrained force are less than 10^(-4).
作者
师恒
王雪莉
谢梅林
曹钰
冯旭斌
廉学正
SHI Heng;WANG Xueli;XIE Meilin;CAO Yu;FENG Xubin;LIAN Xuezheng(Xi'an Institute of Optics and Precision Mechanics of CAS,Xi'an 710119,China;Military Representative Bureau of Equipment Department of Aerospace System Department,Xi'an 710082,China)
出处
《计算机测量与控制》
2023年第12期103-109,共7页
Computer Measurement &Control
基金
中国科学院青年创新促进会基金项目(2021406)
中国科学院空间精密测量技术重点实验室基金项目(29J21-063-Ⅲ)。
