Electromagnetic pneumatic microvalves, widely used in knitting machines, typically operate based on a spring-return mechanism. When the coil is energized, the electromagnetic force overcomes the spring force to attract the armature, opening the valve. Upon de-energization, the armature returns to its original position under the restoring force of the spring, closing the valve. However, most existing electromagnetic microvalves adopt a radially asymmetric magnetic yoke design, which generates additional radial forces during operation, leading to armature misalignment or even sticking. Additionally, the inductance effect of the coil causes a significant delay in the armature release response, making it difficult to meet the knitting machine’s requirements for rapid response and high reliability. To address these issues, this paper proposes an improved electromagnetic microvalve design. First, the magnetic yoke structure is modified to be radially symmetric, eliminating unnecessary radial forces and preventing armature sticking during operation. Second, a permanent magnet assist mechanism is introduced at the armature release end to enhance release speed and reduce delays caused by the inductance effect. The effectiveness of the proposed design is validated through electromagnetic numerical simulations, and a multi-objective genetic algorithm is further employed to optimize the geometric dimensions of the electromagnet. The optimization results indicate that, while maintaining the fundamental power supply principle of conventional designs, the new microvalve structure achieves a pull-in time comparable to traditional designs during engagement but significantly reduces the release response time by approximately 80.2%, effectively preventing armature sticking due to radial forces. The findings of this study provide a feasible and efficient technical solution for the design of electromagnetic microvalves in textile machinery applications.

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments.

Hydraulic thrust cylinders in hard-rock tunnel boring machines (TBMs) often exhibit slow response and sluggish acceleration during start-up, which degrades early-stage tracking performance and limits overall operational accuracy. Most existing studies primarily enhance start-up behavior through advanced control algorithms, yet the achievable improvement is ultimately constrained by the system’s flow–pressure capacity. Meanwhile, reported system-level optimization approaches are either difficult to implement under practical TBM operating conditions or fail to consistently deliver high-accuracy tracking. To address these limitations, this paper proposes a “dual-pump–single-cylinder” control framework for the TBM thrust system, where a large-displacement pump serves as the main supply and a parallel small-displacement pump provides auxiliary flow compensation to mitigate the start-up flow deficit. Building on this architecture, an adaptive compensation algorithm is developed for the auxiliary pump, with its output updated online according to the system’s dynamic states, including displacement error and velocity-related error components. Comparative simulations and test-bench experiments show that, compared with a single-pump scheme, the proposed method notably accelerates cylinder start-up while effectively suppressing overshoot and oscillations, thereby improving both transient smoothness and tracking accuracy. This study provides a feasible and engineering-oriented solution for achieving “rapid and smooth start-up” of TBM hydraulic cylinders.

The Earth Pressure Balance (EPB) shield machine plays a pivotal role in underground tunnel excavation, where precise control of chamber pressure is essential for maintaining tunnel stability and minimizing risks. Traditional EPB control methods heavily rely on operator experience, resulting in delays and limited responsiveness to sudden geological changes. This paper presents an improved EPB mechanism model that builds upon traditional approaches, which primarily consider chamber pressure changes caused by soil volume variations. The improved model further incorporates the effects of excavation face pressure variations, arising from factors such as cutterhead soil extrusion and changing geological conditions. By integrating these additional influences, the model achieves more accurate predictions of chamber pressure. To further enhance performance, a hybrid modeling approach is proposed, combining the improved mechanism model with a data-driven component that compensates for residual prediction errors. The hybrid model is validated using field data from two distinct tunneling projects, demonstrating superior prediction accuracy and generalization capability compared to standalone mechanisms and data-driven models. The results confirm that the proposed hybrid model significantly improves pressure prediction accuracy and provides a more reliable solution for intelligent control of the EPB process.