摘要
为解决具有强耦合性和高度非线性的多关节机器人的轨迹跟踪控制问题,提出了一种切换项增益自调整的自适应模糊神经滑模控制策略(adaptive fuzzy neural sliding mode control strategy)。首先通过L.agrange法建立动力学模型,其次通过引入模糊径向基神经网络(RBF),设计神经滑模控制器,实现对滑模控制等效部分的非线性逼近。同时设计切换部分增益的模糊调整规则,进行自适应调整以更好地补偿不确定项,有效消除外部干扰逼近误差对系统的不利影响,通过Lyapunov方法证明了系统的稳定性。仿真结果验证了该控制策略可进一步削弱抖振,加快响应速度,提高跟踪精度,并在主从式上肢外骨骼康复机器人真实实验中得到了有效应用。
To solve the trajectory tracking problem of multi-joint robot with strong coupling and high nonlinearity, an adaptive fuzzy neural sliding mode control strategy with self-adjusting switching term gain is proposed. Firstly, the dynamic model is established by lagrange method. Secondly, by introducing fuzzy radial basis neural network(RBF), the neural sliding mode controller is designed to realize the nonlinear approximation of the equivalent part of sliding mode control. At the same time, the fuzzy adjustment rule of the switching part gain is designed, and the adaptive adjustment is carried out to better compensate the uncertain term, so as to effectively eliminate the external interference approximation error system unfavorable effect of the system, the stability of the system was proved by the Lyapunov method. The simulation results show that the control strategy cannot only further weaken the chattering of sliding mode control input, but also have some advantages in system response speed and tracking accuracy.
作者
冯钧
孔建寿
王刚
FENG Jun;KONG Jian-shou;WANG Gang(Taizhou Institute,Nanjing University of Science and Technology,Taizhou 225300,China;School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《模糊系统与数学》
北大核心
2021年第5期65-75,共11页
Fuzzy Systems and Mathematics
基金
国家自然科学基金资助项目(61973167)
江苏省自然科学基金面上项目(BK2017803)
泰州市科技计划项目(SSF20210391)。
