School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, Shandong 255049, China.
Rev Sci Instrum. 2022 Aug 1;93(8):084706. doi: 10.1063/5.0099485.
The assembly error of flexible joints and the change in joint stiffness during movement make the actual value of joint parameters inconsistent with the given value, which affects the joint control accuracy. In order to suppress the influence of parameters error, a parameters identification method for flexible joint combined offline identification and online compensation is proposed. First, the offline identification model of inertia, mass, and damping and the online identification model of joint stiffness are established, respectively. Then, a hybrid tracking differentiator based on an improved Sigmoid function is designed to track the differential signals of joint motion parameters, and the Lyapunov function is designed to prove its convergence. The adaptive differential evolution is used as the identification algorithm, and the improved adaptive crossover, mutation factor, and Metropolis acceptance criterion are designed to improve the convergence speed. Finally, a feedforward control structure based on identification is designed to compensate for the model deviation. Simulation and experimental results show that the improved differentiator can effectively improve the tracking speed and derivation accuracy of the signals. Compared with other algorithms, the proposed identification method has a faster convergence speed and higher identification accuracy, and feedforward compensation control can effectively correct model parameters and improve control accuracy.
柔性关节的装配误差以及运动过程中关节刚度的变化使得关节参数的实际值与给定值不一致，从而影响关节的控制精度。为了抑制参数误差的影响，提出了一种结合离线辨识和在线补偿的柔性关节参数辨识方法。首先，分别建立了惯性、质量和阻尼的离线辨识模型以及关节刚度的在线辨识模型。然后，设计了一种基于改进 Sigmoid 函数的混合跟踪微分器来跟踪关节运动参数的微分信号，并设计了 Lyapunov 函数来证明其收敛性。采用自适应差分进化作为辨识算法，并设计了改进的自适应交叉、变异因子和 Metropolis 接受准则，以提高收敛速度。最后，设计了基于辨识的前馈控制结构来补偿模型偏差。仿真和实验结果表明，改进的微分器可以有效地提高信号的跟踪速度和导数精度。与其他算法相比，所提出的辨识方法具有更快的收敛速度和更高的辨识精度，前馈补偿控制可以有效地修正模型参数，提高控制精度。
