Optimization of the excitation trajectory of particle gray wolf optimization algorithm

Abstract

Aiming at the excitation trajectory design in the identification of inertial parameters of industrial robots, this paper proposes a step-by-step identification and particle gray wolf optimisation algorithm (PSOGWO) to optimise the design of excitation trajectory parameters. First of all, the robot's minimum inertial parameter observation matrix is derived and established by Newton-Eura recursive method, and the observation matrix condition number criterion is used as the optimisation objective function; secondly, the particle gray wolf optimisation algorithm (PSOGWO) is introduced; finally, the periodic Fourier series that meets multi-constraint conditions is optimised and designed as the incentive trajectory using the particle gray wolf optimisation algorithm (PSOGWO). Experimental results show that the excitation trajectory designed with the proposed optimisation method can fully stimulate the dynamic characteristics of the robot, improve the anti-noise ability of parameter identification, and lay a foundation for accurately obtaining the dynamic parameters of the robot.