Nonlinearities and unknown disturbances are widely present in mechanical systems. This study investigates an improved nonlinear extended disturbance observer and its application in electro-hydraulic servo system, which represents a typical nonlinear system. The focus lies in estimating the external disturbance and its nth-order derivates of the system to enhance the robustness and precision of the control system. Additionally, we introduce a high gain observer dedicated to estimating the system's velocity. Finally, we establish a sliding mode controller based on the improved disturbance observer and high gain observer, aiming to mitigate the impact of external force disturbances. Comparative experiments have been conducted to prove the effectiveness of this approach.

High-precision position control of hydraulic systems is of great significance in industrial applications. However, unknown external force disturbance, measurement noise, and other uncertain nonlinearities widely exist in electro-hydraulic servo systems, which severely degrade the system performance. To handle this problem, a sliding-mode controller based on K-observer with nonlinear disturbance observer is proposed for electro-hydraulic position systems in this article. The nonlinear disturbance observer is to estimate and eliminate the time-varying external force disturbance. The sliding-mode controller based on K-observer is to reach low gain observation of states and improve the system performance by reducing the chattering of the sliding-mode algorithm. The proposed controller is proved to be stable by the Lyapunov stability criterion. Comparative experiments support that the controller has high precision and strong robustness.

Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading.