Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator

During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable. In this paper, modeling and automation approaches for a hydraulic mini excavator are developed. In particular, a physical model for detailed system analysis and a simplified Hammerstein model for controller tuning are developed and validated with measurement data from the mini excavator. For attitude estimation of the excavator, inertial measurement units and extended Kalman filters are used in a sensor fusion framework. The control concept for automation is based on a virtual driver consisting of a state machine for task coordination as well as offset-free model predictive controllers (MPCs) for decentralized and robust tracking control of all motion axes. The constrained MPC optimization problems are solved in real time by means of the accelerated proximal gradient method. Experimental results from the mini excavator prove the developed control approach to be valuable for virtual development and automated testing during the commissioning of hydraulic machinery.