Mass estimation of a simple hydraulic crane using discrete extended Kalman filter and inverse dynamics for online identification

Abstract

Automatization of hydraulic machinery requires accurate information of the current dynamic state of the machinery but also information of the underlying dynamic model characterized by a set of parameters. Some of the parameters can be considered static and well defined, such as machinery dimensions, whereas a part of the parameter set is time varying and needs to be identified based on observations. Particularly, difficult parameters to estimate are the ones, from which no prior knowledge is available. Consequently, the parameter corrections cannot be assumed to be small, which is commonly required for the existing parameter estimation algorithms. This study creates an online capable identification algorithm for estimation of a load mass operated by a hydraulic crane. In the case of load mass estimation, the unknown parameter can be practically any positive value, which implies the parameter corrections to be large. In this study, the estimation problem is divided in two parts: First, the dynamical states of the system are estimated based on the system kinematic relationships and dynamics of the hydraulic circuit. Secondly, the unknown load mass is estimated based on the known hydraulic forces and kinematics using the inverse dynamics of the mechanical structure. The proposed algorithm is tested with both artificially created measurements and with an experimental setup. The results show that both the kinematics of the structure and hydraulic pressures can be accurately estimated using the proposed method. Moreover, the method can be used to further estimate the payload mass. A drawback related to inverse dynamics is that it produces biased estimates in static equilibrium because of the discontinuous nature of static friction force. However, this drawback can be avoided, in part, by not updating the payload estimate in the low-velocity region. The proposed estimation methodology is capable for online identification, and as such, it can be used to adapt the control laws of automated machinery. Moreover, the methodology can be useful to record and document the amount of payload being handled during a work cycle.