Comparison of joint angle, velocity and acceleration estimators for hydraulically actuated manipulators to a novel dynamical approach. (October 2019)

• Record Type: Journal Article
• Authors: Wind, Hannes; Renner, Anton; Schaut, Stefan; Albrecht, Simon; Sawodny, Oliver
• Is Part Of: Control engineering practice. Volume 91(2019)
• Publication Date: 2019-10
• Subjects: Dynamics -- Hydraulic manipulator -- Kalman filtering -- Kinematics -- Sensor fusion; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2019.104118
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Excavators are used for a wide range of applications like earthworks and material handling. Assistance systems are becoming more common to support the operator. For monitoring and control based assistance functions the angular position, velocity and acceleration of the joints from the working implement are required. Commercial systems often use inertial measurement units, consisting of triaxial accelerometers and gyroscopes, to accomplish an estimation of those states. A novel joint angle, velocity and acceleration estimation for hydraulic manipulators is proposed and compared to state of the art methods. A decentralized kinematic filter using no information about the underlying system and a centralized kinematic filter taking the system kinematics into account are implemented as state of the art approaches. Both filters only use inertial measurement units to obtain information about the current state of the system. The novel centralized dynamic filter uses the same information as the centralized kinematic filter and extends it by a dynamic model containing additional information about the angular acceleration due to pressure readings of the hydraulic cylinders. Kalman filtering is used to combine the derived system and measurement models with the sensor information. The methods are evaluated on a material handling excavator for single and coupled movements of the working implement. The novel centralized dynamic filter enables improvements for the angular acceleration estimation compared to the decentralized and centralized kinematic filter. Less noise of the acceleration estimation and a better tracking of the actual acceleration are shown. Graphical abstract: Highlights: Joint angle, velocity and acceleration estimation for hydraulic manipulators. Sensor fusion using pressure sensors, IMUs, kinematic and dynamic model. Comparison of state-of-the-art estimators with novel dynamic filter approach. Experimental results on a material handling excavators are shown. Novel approach shows best acceleration estimation for the considered experiments.