Parameter Identification of Collaborative Robot Based on Improved Artificial Fish Swarm Algorithm

In order to achieve precise control of collaborative robots, it is necessary to obtain an accurate model for the robot. Identification of dynamic parameters is one of the important ways to obtain accurate models. To identity the dynamic parameters. This paper proposes an identification method based on an improved artificial fish swarm algorithm. First, to describe the joint friction more effectively, a friction model with compensation is introduced. The dynamic model of the robot employs the Lagrangian method, and the model is appropriately transformed to determine the minimum inertia parameter set. A 6 degree-of-freedom robot is simplified using the connected assembly method, then the minimum inertial parameter set is reorganized. Second, the joint trajectory is designed with the Fourier series as the basic function form of the trajectory, the condition number of the observation matrix is used as the objective function, then using the improved artificial fish swarm algorithm to optimize the excitation trajectory. Finally, we use MATLAB for simulation, and a 6 degree-of-freedom collaborative robot independently developed by the institute. The results show that the predicted joint torque obtained from the identification was highly matched with the actual torque, which proved the effectiveness of the proposed method.