Identification of collaborative arm dynamics parameters based on improved hippo algorithm

For the traditional kinetic parameter identification methods face the problems of low accuracy, slow convergence speed and poor robustness. To this end, this paper proposes the Improved Hippo Algorithm (IHO), which firstly introduces the Tent chaotic mapping to initial the hippo population to improve the global search capability; and combines the original random escape strategy with the triangular escape strategy to enhance the local search capability of the algorithm. An adaptive convergence factor based on the cosine function is introduced to balance the exploration and development phases of the algorithm and improve the convergence speed and overall performance of the algorithm. Dynamic parameter identification experiments are conducted on a real robot, and the results show that the IHO algorithm has faster identification speed and higher identification accuracy than the traditional RLS algorithm.