In impedance control of hydraulic excavators the piston position and ram force of each hydraulic cylinder for the axis control of the boom, arm, and bucket can be determined. The problem is thus how to find the control voltage applied to the servovalves to track these commands to the hydraulic systems. This paper presents analytic, simulation and experimental results for controllers that have been developed in our laboratory to achieve force and position tracking of clectrohydraulic systems of a robotic mini-excavator. The systems with hydraulic cylinders as actuators are represented by a comprehensive model taking into account friction, nonlinearities, and uncertainties. A discontinuous observer is developed for estimating both piston velocity and disturbance force including friction. With an observer-based compensation for disturbance, tracking of the piston rain force and position is guaranteed using a robust sliding mode controller. The control signal consists of three components: equivalent control, switching control, and fuzzy control. High performance and strong robustness can be obtained as demonstrated by simulation and experiments performed on a hydraulically-actuated Komatsu PC05-7 robotic excavator. Promising results are reported, and issues relating to future work are discussed.

This paper presents global strategies for the computer control of autonomous backhoe-type excavators. The control structure is divided into low and high levels. The low level control utilises fuzzy logic to encapsulate expert experience for capturing soil in many excavation scenarios. UML statecharts are used at the higher level for mapping environment and machine sensor data to actuator control signals. This mapping is based on a deep understanding of excavation performed by a skilful operator and is coded into rule sets. Transition between states or behaviours is accomplished via associated task characteristic functions that not only switch among tasks but also enable/disable rules according to digging phases. Typical excavation tasks are decomposed into statecharts and task elements. The control schemes are illustrated by autonomous trench digging. Field test results are provided to verify the validity of the proposed control architecture.