This article focuses on the asymptotic adaptive appointed-time performance control for electro-hydraulic servo systems subject to unknown inconstant linearizable parameters, nonlinear parameters, and disturbances. To overcome this issue, uniting the adaptive control with an appointed-time performance function, an adaptive appointed-time performance controller is developed for electro-hydraulic servo systems, in which unknown inconstant linearizable parameters, nonlinear parameters, and disturbances can be addressed simultaneously. This relieves the assumption that unknown parameters should be constant and linearizable in most of the existing adaptive control results. Moreover, an introduced appointed-time performance function can guarantee that the tracking error is restricted to a prescribed boundary and converges to a preset region within an appointed time without any initial performance restriction. Theoretical analysis reveals that both the asymptotic steady performance and the appointed-time performance of the tracking error could be achieved with this presented method. Comparative experimental results also reveal the merits of this method.

Electro-Hydrostatic Actuators (EHAs), as complex integrated systems of mechanical, electrical, and hydraulic components, play a crucial role in aerospace and other fields. However, due to the complexity of their internal structure and harsh working environments, the compound faults are the most common fault types. Considering the high cost of data collection for uncertainties and complex compound faults in EHA, an intelligent decoupling diagnosis method for compound faults in EHAs based on Maximized Aggregation Attention Convolutional Capsule Network (MAACCN) can be proposed. Firstly, the multi-dimensional sensor information from the EHA can be collected, the feature-level data is fused through a one-dimensional convolutional layer combined with an efficient channel attention mechanism. Secondly, it utilizes the capsule network to extract features deeply and introduces a maximized aggregation routing algorithm between capsule layers. Finally, a decoupling classification layer can be added, its model is optimized by minimizing the margin loss function, enabling the network to more accurately identify and decouple compound faults. Validation on the EHA fault dataset demonstrates that the proposed method achieves higher subset accuracy under different working conditions, which can diagnose compound faults by learning data from single faults.

The electro-hydrostatic actuator (EHA), the actuator of electric drive and hydraulic transmission, is competitive since it is small in size, light in weight and high in power density. However, the existence of the velocity loop error of servo motors, unmodeled dynamics and highly nonlinear uncertainties restrict the improvement of the tracking accuracy of the EHA system. In order to achieve high-precision motion control of EHAs, a RISE-based composite adaptive control scheme is proposed in this paper. In the proposed composite adaptive control design, a novel parameter adaptive law is synthesized to compensate for the parametric uncertainties and a robust integral of the sign of error (RISE) feedback is utilized to suppress the adverse effects caused by the lumped disturbances, including the velocity loop error of a servo motor and other unmodeled dynamics. The synthesized parameter adaptive law possesses the advantage of fast convergence, which is beneficial to achieve transient tracking performance improvement. In addition, the proposed controller is more suitable for practical applications since it is chattering free. The closed-loop system stability analysis shows that the proposed control scheme guarantees an excellent asymptotic tracking performance. Finally, comparative simulations are conducted to verify the high-performance nature of the proposed controller.