For the social robot with serial elastic actuator, the joint dynamics model has the problems of strong coupling and high nonlinear, and the traditional PD control algorithm cannot achieve accurate trajectory tracking effect on the joint position of social robot using series elastic actuator. Therefore, an optimized Radial basis function (RBF) neural network adaptive control algorithm was proposed. The method based on RBF neural network approximates the social robot joint model parameters, an adaptive law was designed to estimate the weights of the neural network and the joint model online. The dynamic plane method is combined to improve the robustness of the control algorithm. The simulation results show that the trajectory tracking error peak of PD control algorithm is 0.2 rad. Compared with PD control algorithm, the trajectory tracking error peak of RBF neural network adaptive control algorithm based on dynamic surface optimization is reduced to ± 0.05 rad, which realizes accurate approximation of the parameters of social robot joint model, and accurate dynamics model approximation provides a theoretical basis for further research on human–robot interaction (HRI) of social robots.

对于具有串联弹性致动器的社交机器人，其关节动力学模型存在耦合性强，非线性高的问题，传统的PD控制算法无法对采用串联弹性致动器的社交机器人的关节位置实现精确的轨迹跟踪效果。因此，提出了一种优化的径向基函数神经网络自适应控制算法。基于RBF神经网络的方法对社交机器人关节模型参数进行了近似，设计了一种自适应律，用于在线估计神经网络和关节模型的权重。结合动态平面方法来提高控制算法的鲁棒性。仿真结果表明，PD控制算法的轨迹跟踪误差峰值为0.2rad。与PD控制算法相比，