Parameter Identification of a Shell Transfer Arm Using PSO and Similarity Degree Analysis

Abstract

Abstract-A shell transfer arm is a complex mechanical electro-hydraulic system with multiple parameters. An accurate dynamic model is the precondition of improved design. However, during the modeling process, several parameters that have a significant effect on the performance of the system can be achieved only by identification methods because they are hard to measure. According to the structure and control theory of the shell transfer arm, the analytical model and control model are built. To evaluate the level of similarity of the built model and real system, the numerical similarity and shape similarity of the angular velocity of the arm are calculated, and the combination similarity of numerical similarity and shape similarity is set as the fitness function of Particle Swarm Optimization (PSO). Four unknown parameters are identified by PSO. The identification results with respect to the simulation data show that the identification accuracy of PSO can acquires the requirement. And the identification results with respect to the test data show that the built model is accurate, and parameter identification of the shell transfer arm based on PSO and similarity degree analysis of time-vary data is feasible and effective.Index Terms-Shell transfer arm, dynamic modeling, parameter identification, particle swarm optimization, similarity degree analysis.

Contact parameters identification of a cannon cradle and its bushing based on FDA and ELM

“…Since it is difficult to find the analytic expressions of the measurable response to the parameters to be identified, optimization method based on analytic expression cannot be used to identify the contact parameters of the cradle and its bushing [1]. Machine learning (ML) can estimate the dependence of data based on the known sample, so ML can predict and judge the unknown data.…”