State Estimation for a portal advancing mechanism by measuring the pressure in hydraulic actuators

Leg-driven machines require complex control systems to ensure stability and safety. Instead of measuring the complete system state, state estimation and prediction can be used to observe the system state in hydraulically actuated systems to replace expensive sensors. In this paper, we consider the portal advancing mechanism, a legged locomotive mechanism. The overall machine consists of two bases with three hydraulically actuated legs each and a bridge on top of them. An upper carriage with a forestry crane can move along the bridge. A system is proposed that estimates the position of the carriage. The Extended Kalman Filter (EKF) handles the non-linearity of the system and provides information about the uncertainty of the state estimation. The load on the hydraulically actuated legs is different for every carriage position on top of the mechanism. We assume that the dynamic effects of the movement can be modeled with a linear model. The state is described by the position, velocity, and acceleration of the center of gravity of the machine’s upper carriage. The non-linear relation between the state vector and the pressures in the hydraulic actuators is linearized in the innovation step of the applied filter. ... mehrThe proposed system is evaluated in a multibody machine simulation with hydraulic co-simulation. The proposed system reduces the number of required sensors on this type of walking forestry machine as the movements do not need to be captured directly. The results show that modern state estimation and prediction can support predictive control of complex, dynamic hydraulically driven systems.