Li Ye, Wang Dazhi, Du Mingtian, Zhou Shuai, Cao Shuo, Li Yanming
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Department of Mechanical Engineering, The University of Melbourne, Victoria 3010, Australia.
Comput Intell Neurosci. 2022 Aug 22;2022:8151132. doi: 10.1155/2022/8151132. eCollection 2022.
Effective and accurate parameter identification, especially the identification of load torque, is one of the key factors to improve the control performance of the robot servo system. Sliding mode observer (SMO) has always been a common method for identifying load torque due to its advantages of simple implementation, strong robustness, and fast response. However, due to the discontinuity of the SMO switching function, the system will generate high-frequency chattering, which will reduce the accuracy of load torque identification and affect system performance. In this paper, an adaptive parameter identification method based on an improved sliding mode observer is proposed. A continuous deformation mode of saturation function based on boundary variation is proposed as the switching function to alleviate the chattering phenomenon. Meanwhile, the relationship between the sliding mode gain and the feedback gain of proposed SMO is defined so that it can be selected properly to improve the accuracy of identification, and the radial basis function neural network (RBFNN) is used to adaptively tune the boundary layer gain according to the speed change. Moreover, considering that the identification result of the load torque is related to the moment of inertia and the mismatch of the inertia will cause identification errors, the variable period integration method is proposed to identify the inertia and redefine the calculation period of the load torque and inertia. The effectiveness and superiority of the proposed method are verified by simulation experiments. Experimental results demonstrate that the improved SMO combines observer gain coefficient tuning and inertia matching can smoothly and accurately estimate the value of load torque, which is an adaptive identification method worthy of reference for robot servo system.
有效且准确的参数识别，尤其是负载转矩的识别，是提高机器人伺服系统控制性能的关键因素之一。滑模观测器（SMO）因其实现简单、鲁棒性强和响应速度快等优点，一直是识别负载转矩的常用方法。然而，由于SMO切换函数的不连续性，系统会产生高频抖振，这将降低负载转矩识别的精度并影响系统性能。本文提出了一种基于改进滑模观测器的自适应参数识别方法。提出了一种基于边界变化的饱和函数连续变形模式作为切换函数，以减轻抖振现象。同时，定义了所提SMO的滑模增益与反馈增益之间的关系，以便能够合理选择它们来提高识别精度，并利用径向基函数神经网络（RBFNN）根据速度变化自适应调整边界层增益。此外，考虑到负载转矩的识别结果与转动惯量有关，转动惯量不匹配会导致识别误差，提出了变周期积分法来识别转动惯量，并重新定义了负载转矩和转动惯量的计算周期。通过仿真实验验证了所提方法的有效性和优越性。实验结果表明，改进后的SMO结合观测器增益系数调整和惯量匹配能够平滑且准确地估计负载转矩的值，是一种值得机器人伺服系统参考的自适应识别方法。
