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.

In the fully mechanized mining face, proper chain tension of chain transmission system is of great significance in reducing the failure rate and improving the service life of scraper conveyor. However, the disturbance caused by random load and variable stiffness makes it very difficult to control chain tension. An adaptive tension control scheme combining a neural command filtering backstepping algorithm and online identification is proposed in this article based on a novel tension model. First of all, the proposed tension model is represented by a linear regression part and a nonlinear mapping part, which effectively reflects the chain transmission characteristics and avoids the full measurement of each chain ring. Then, Levant's differentiator and state observer are used to expand input variables for identification algorithm and neural networks estimation. To avoid the adverse effect of random load on parameter adaption, stiffness online identification algorithm is employed in improving the tension tracking performance. To compensate for nonlinear mapping part and uncertain nonlinearity caused by random load, neural adaptive estimator is effectively integrated with the robust integral term in backstepping design. Moreover, Lyapunov stability of the proposed controller is guaranteed. Finally, comparative experimental results of six controllers are completed to verify the effectiveness of the proposed controller in the static and random load mode.

The fusion of inertial navigation systems (INS) and ultrawideband (UWB) is a primary approach to enhance the positioning accuracy and stability of mine-used underground monorail cranes (MUMC) in coal mine tunnels. UWB demonstrates high positioning accuracy in ideal conditions. It is, however, susceptible to nonline-of-sight (NLOS) effects. Meanwhile, INS is unaffected by external environmental conditions but suffers from cumulative errors. To address these challenges and enhance the positioning accuracy and stability of MUMC, this article proposes an angle residual weighted adaptive Kalman filtering (ARWAKF) for MUMC positioning based on UWB/INS integration. The angular residuals are designed to assign weights to INS and UWB in the fusion algorithm, which rely on the geometric relationship of the UWB network and the motion state of MUMC. Experimental results demonstrate the superior performance of the proposed positioning method compared to INS, UWB, and extended Kalman filtering (EKF).