Dynamic Modeling and Load Identification of Industrial Robot Using Improved Particle Swarm Optimization

Abstract

The precise model identification is one of the key technologies for the high-performance control of a multi-joints industrial robot. In this paper, an improved particle swarm optimization algorithm (IPSO) with a cross-mutation function is presented to estimate the robotic dynamic parameters. This proposed algorithm can avoid the final solution trapping into local optimum, and the identification precision is improved significantly. Firstly, the theoretical model is deduced on the basis of the robotic load dynamic parameters. Then, the IPSO solution is derived to identify the load dynamic parameters achieving a global optimum solution. Thus, the complete robotic dynamic model can be established. The effectiveness of the proposed load identification method is verified by experiments on a real-time industrial robot. As compared with the traditional method, we show that the proposed method maintains superior identification accuracy.