In the new era of intelligent manufacturing, renewable energy systems and other technological advancements, motor/hydraulic-actuator-based electromechanical servo systems have become a key foundation for obtaining high-performance control. Therefore, the high-performance control of electromechanical servo systems based on motor/hydraulic actuators is important [1,2].
However, high-performance controller design for these systems remains a challenging task. Motor/hydraulic actuators are inevitably affected by modeling uncertainties, which may degrade control performance or even make the whole closed-loop system unstable [3]. Therefore, this Special Issue highlights prescribed performance control (PPC), preset-time control (PTC), state constraint control, input constraint control and so on as important approaches for meeting increasingly strict system control requirements.
PPC is an effective method for guaranteeing both transient and steady-state tracking performance of electromechanical systems. Xu et al. proposed an output-feedback PPC method for state-constrained systems and verified its effectiveness via a motor system [4].
PTC is another important topic for high-performance servo systems. Hernandez-Gonzalez et al. proposed a PTC method and applied it to a two-link robot arm. The proposed controller employs a logarithm function in its design; its convergence time is defined by the user and is independent of initial conditions and any other system parameters [5].
State constraint control is a key factor in ensuring safety and reliability of electromechanical servo systems. Xu et al. proposed a barrier Lyapunov function-based adaptive output-feedback controller for hydraulic systems with prescribed performance and uncertainty compensation [6].
Input constraint control is also a major issue in high-performance electromechanical servo control. Heydari Shahna et al. proposed a robust decomposed system controller for an electromechanical linear actuator mechanism under input constraints, in which the actuator is driven by a permanent magnet synchronous motor with modeling uncertainties [7].
In recent years, a number of representative studies on advanced control methods for servo systems have been proposed. Phan and Ahn addressed the coexistence of multisensory faults and external disturbances in electro-hydraulic servo systems by proposing a fault-tolerant controller that integrates a nonlinear unknown input observer, an extended state observer, and dynamic surface control [8]. Zhou et al. studied the synchronization control problem of an electro-hydraulic dual-cylinder system and proposed an adaptive robust synchronization controller with dynamic thrust allocation [9]. Wang et al. further proposed a reinforcement learning controller for electro-hydraulic systems [10]. In addition, Wan et al. considered the problems of model uncertainty and full-state constraints in electro-hydraulic servo systems and proposed an adaptive neural controller based on a nonlinear disturbance observer [11].
This Editorial relates to a Special Issue focused on high-performance control of electromechanical servo systems based on motor/hydraulic actuators. The Special Issue highlights new opportunities and challenges for improving system performance, with particular emphasis on advanced control methods under complex conditions. The studies included in this Special Issue were selected through a rigorous peer-review process. The main contributions of these works are concluded as follows.
Advanced control methods remain a central driving force for improving system performance, and six papers in this collection specifically address this issue.
Xu et al. addressed the problems of insufficient response speed, limited control accuracy, and degraded disturbance rejection in permanent magnet synchronous motors operating under segmented structures and high-frequency disturbances by proposing a fast terminal sliding-mode controller based on a novel fixed-time sliding surface.
Guo et al. focused on a typical trade-off in finite-control-set model predictive current control for permanent magnet synchronous motors, namely that shortening the control period can improve current tracking but usually leads to higher switching frequency and increased losses.
Han et al. investigated the trajectory tracking problem of servo hydraulic cylinders by integrating reinforcement learning with a predefined-time control framework. The proposed method can obtain fast response performance.
In another contribution, Han et al. addressed the more challenging problem of pressure control in electro-hydraulic servo systems by proposing an adaptive output-feedback pressure controller.
Dong et al. focused on the problem of high-precision force control in an electro-hydraulic proportional load simulator. They constructed a finite-time prescribed performance function and incorporated neural networks together with an output-feedback-only design.
Qiu et al. extended the investigation to a pump-controlled automatic gauge control system for lithium battery pole strip mills, with particular emphasis on two typical nonlinear factors: input dead zone and hydraulic-cylinder friction. Based on the LuGre friction model and a dead-zone model, they established a mathematical description of the system and achieved accurate compensation by combining a sliding-mode observer with adaptive backstepping control.
Beyond control methods, the issues of modeling complex actuation systems and structural innovation in specialized actuating mechanisms are also represented in this collection, with two papers exemplifying these two directions.
Lyu et al. focused on the main distributing valve of a hydro-turbine governing, a complex hydraulic component, and proposed a mechanistic–data-driven hybrid modeling method. A Bayesian optimization-enhanced light gradient boosting machine was employed to estimate the unknown dynamics.
Dang et al. developed a single-degree-of-freedom four-bar planting manipulator to address the limitations of existing vegetable pot–seedling transplanting mechanisms, which are often either structurally complex and costly or relatively simple but inadequate in planting quality.
Future research directions including event-triggered control (ETC), optimal control, and so on. ETC provides a promising framework for reducing communication, computation, and actuation burden in networked and embedded servo control systems [12,13]. For motor/hydraulic servo systems with limited computational resources, networked sensors, or distributed controllers, ETC can reduce unnecessary controller updates while maintaining stability, robustness, and tracking performance. Optimal control is another important research direction, aiming to further optimize control energy, actuator effort, and overall performance while ensuring system stability and tracking accuracy [14]. For motor/hydraulic-actuator-based servo systems, optimal control can not only improve trajectory tracking performance but also realize a comprehensive trade-off among energy consumption, control input, and response time.
Funding
This research received no external funding.
Data Availability Statement
Not applicable.
Conflicts of Interest
The author declare no conflict of interest.
List of Contributions
- Xu, Q.; Wang, G.; Fang, S. Fast Terminal Sliding Mode Control Based on a Novel Fixed-Time Sliding Surface for a Permanent Magnet Arc Motor. Actuators 2025, 14, 423. https://doi.org/10.3390/act14090423.
- Guo, Y.; Jiang, F.; Wang, S.; Cheng, S.; Hu, Z. Variable Control Period Model Predictive Current Control with Current Hysteresis for Permanent Magnet Synchronous Motor Drives. Actuators 2025, 14, 517. https://doi.org/10.3390/act14110517.
- Han, T.; Nie, X.; Que, N.; Lu, J.; Yao, J.; Yu, X. Predefined-Time Tracking Control of Servo Hydraulic Cylinder Based on Reinforcement Learning. Actuators 2026, 15, 9. https://doi.org/10.3390/act15010009.
- Han, T.; Lu, J.; Ye, J.; Wang, W.; Yao, J.; Yu, X. Adaptive Output Feedback Pressure Control for Electro-Hydraulic Servo Systems. Actuators 2026, 15, 10. https://doi.org/10.3390/act15010010.
- Dong, Z.; Li, C.; Zhang, P.; Jia, Y.; Yao, J.; Liu, L. Finite-Time Prescribed Performance Neural Network Force Control of Electro-Hydraulic Proportional Load Simulator with Output Feedback. Actuators 2026, 15, 150. https://doi.org/10.3390/act15030150.
- Qiu, G.; Hao, Y.; Chen, G.; Yan, G.; Chen, Y. Adaptive Backstepping Control for Battery Pole Strip Mill Systems with Friction and Dead-Zone Input Nonlinearities. Actuators 2025, 14, 618. https://doi.org/10.3390/act14120618.
- Lyu, Z.; Guo, J.; Wu, S.; Wei, Z.; Lyu, J.; Zhang, K. Dynamic Mechanistic–Data-Driven Hybrid Modeling of the Main Distributing Valve in a Hydroturbine Governor. Actuators 2025, 14, 572. https://doi.org/10.3390/act14120572.
- Dang, Y.; Jiang, G.; Zhang, Y.; Zhou, Z. Design and Experiment for a Single-Degree-of-Freedom Four-Bar Planting Manipulator. Actuators 2026, 15, 207. https://doi.org/10.3390/act15040207.
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