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

TLDR: 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 and that the introduced framework can easily be extended in order to automate other types of machinery with simple hydraulics.

Abstract: 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 Note to Practitioners —In this paper, a hydraulic mini excavator is considered for demonstrating the benefits of automation with regard to the development of advanced mobile machinery A detailed physical model and a simplified model are introduced for virtual analysis and comissioning of the excavator This allows for detailed system analysis even at an early development stage Then, a framework for automated testing of the real prototype is introduced This concept is based on attitude estimation filters, a state machine, and model predictive controllers and closely resembles the human driver in its behavior, but allows for reproducible testing results and therefore reduces commissioning efforts and development costs Particular attention is paid to the robustness of the control concept, since the coupling of the hydraulic axes and digging forces lead to disturbances that need to be compensated A simple, yet efficient and real-time capable algorithm is provided for numerical optimization Experimental results show that the developed methods can contribute to the automation of hydraulic machinery, and that the introduced framework can easily be extended in order to automate other types of machinery with simple hydraulics