Dynamic Parameter Identification of Industrial Robot Based on Improved Immune Clone Selection Algorithm

This paper proposes a method for dynamic parameter identification of unloaded industrial robots based on an improved immune clone selection algorithm (ICSA). Firstly, the dynamic model of serial industrial robot is constructed based on Newton-Euler method, and then the model is linearized. Secondly, the identification excitation trajectory is designed and optimized under the condition of satisfying the boundary constraints. Finally, in order to improve the convergence speed of the algorithm, the clonal mutation and apoptosis mechanism of the basic ICSA algorithm are improved. Simulation results show that the proposed method has better recognition performance compare with basic ICSA and least squares methods (LSM).