); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (C.H.) [PERSON]  
1 Guangzhou Institute of Advanced Technology, Chinese Academy of Science, Guangzhou 511458, China; [EMAIL_ADDRESS] (G.Z. ); [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (W.Y. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (G.Y. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (C.H.) [PERSON]DRESS] (G.Y. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (H.W. ); [EMAIL_ADDRESS] (C.H.) [PERSON]  
1 Guangzhou Institute of Advanced Technology, Chinese Academy of Science, Guangzhou 5111458, China; [EMAIL_ADDRESS] (G.Z. ); [EMAIL_ADDRESS] (Z.H. ); [EMAIL_ADDRESS] (W.Y. ); jm.liang@gitat. For dynamic models of multi-axis serial chain robots, gravity, Coriolis force, inertial force and friction are generally considered. However, the reducer flexibility, inertia force of motor rotors, etc. are rarely taken into consideration. In addition, a precise dynamic model of the robot manipulator relies heavily on an accurate friction model [3]. Accurate friction models cause some error between the calculated torque and the actual torque, which hinders the good realization of collision detection, lead-through programming, and compliance control. Many researchers have proposed different error compensation policies [4; 5]. Machine learning methods, such as back propagation (BP) neural network [6] and radial basis function (RBF) network [7; 8], are also used to fix or compensate errors. [PERSON] et al. [9] present a novel dynamic modeling method by using a recurrent neural network (RNN) with an incomplete state system variables observation. The RNN family are also used for robotic manipulator model learning tasks [10] and approximating robot dynamical models [11; 12]. However, studies with many kinds of complex characteristics combined are limited. As for parameter identification, there is the disassembly measurement calculation method [13], the disassembly experiment measurement method [14], the non-disassembly experiment measurement method [15], and theoretical identification [16], etc. The disassembly method cannot, or has difficulty in, calculating the characteristics of robot joints [17]. The inertial tensor matrix obtained by the theoretical identification method may not necessarily be positive or definite under any form or situation. It is impossible to exist in practice, and its control is unstable [18]. To this end, an optimized RNN is proposed to compensate the error of the collaborative robot dynamic model, and the pipeline is as follows. First, the dynamic equations are established, and then the non-disassembly experimental measurement combined with the least square method is used to identify the parameters. Next, in order to tackle unmodeled characteristics or the inaccurate modeling of collaborative robots, an optimized RNN is put forward. With this method, the errors of the dynamic model are reduced, and accuracy of torque prediction is improved. In the final experiment, a motor current is used to evaluate the joint torque instead of mounting expensive and inconvenient torque sensors to fulfill the verification of the compensation method. This paper is organized as follows: Section 2 presents the dynamic model of the self-developed six-degree-of-freedom (6-DOF) collaborative robot; Section 3 shows the excitation trajectory generation; Section 4 presents the error compensation by an optimized recurrent neural network; Section 5 shows the results of the experiment using a self-developed 6-DOF collaborative robot; Section 6 concludes the paper.