Shangbin Jiao

Rapid removal of ammonium bisulfate (ABS) deposits requires both high-efficiency electromagnetic heating and stable thermal regulation under continuously varying operating conditions. To address this challenge, this paper proposes a resonant ablation equipment (RAE) integrating electromagnetic-thermal coupling analysis, load configuration optimization, and dynamic resonance control. A discrete electromagnetic–thermal model is first established to reveal the relationship between the magnetic energy distribution and heat generation, on which a multi-objective optimization strategy is subsequently developed to determine the optimal annular load configuration. Furthermore, a parameter-dynamic controller that combines dynamic resonance tracking (DRT) and automatic temperature control (ATC) is proposed to maintain resonance and suppress thermal inertia during rapid heating. Both theoretical and experimental results demonstrate that optimized annular loads consistently outperform conventional disk loads and that different optimization objectives yield distinct optimal annular configurations. The fastest-heating configuration achieves an average heating rate of 3.878 °C/s, corresponding to 4.02 times that of a conventional disk load, while the efficiencies of all optimized annular configurations are significantly higher than that of the conventional disk load. Moreover, the proposed controller reduces the thermal response delay by approximately 10 s while maintaining stable zero-voltage switching and resonant operation. The proposed framework establishes a complete model-driven methodology for electromagnetic heating systems and offers a novel solution for rapid, controllable removal of ABS deposits.

This paper proposes a novel modeling and control strategy for three-phase voltage source converters (VSCs) based on a switched system framework. A four-dimensional (4D) switched model and state-dependent switching control strategy with dwell time regulation are proposed. The key contributions of this work are: (1) The proposed switching model accurately represents both the continuous and discrete dynamics of the AC current and DC voltage in three-phase VSCs without relying on linearization or approximation techniques. (2) The proposed method enables the simultaneous control of three-phase AC currents and DC voltage within a single loop under the switching control framework. Complex phase-locked loops (PLLs), pulse width modulation (PWM), and the control parameter tuning process are avoided. (3) The steady-state and transient performance of the system was enhanced through the adaptive adjustment of the dwell time of the switching signal. The simulation and experimental results confirm the effectiveness and advantages of the proposed method.

The accurate measurement of complexity in nonlinear signals is crucial for the condition monitoring and fault diagnosis of mechanical systems. While Dispersion entropy (DE) and multiscale DE (MDE) are commonly used for time series complexity analysis. However, DE suffers from two inherent limitations: it requires manual selection of the category number and lacks robustness to noise-induced symbol fluctuations. To overcome these limitations, this work introduces regularized DE (RDE), which automatically optimizes the category number through the Akaike information criterion; and the symbol series regularization is introduced to effectively suppress noise-induced symbol mutation and improves the robustness of entropy estimation. And MDE gradually loses signal fidelity as the scale factor increases. To overcome these limitations, the refined composite Hermite interpolation multiscale RDE (RCHIMRDE) was proposed, which employs polynomial-based signal reconstruction to preserve signal fidelity across multiple scales, addressing the information loss issue in conventional multiscale measurement approaches. Simulated signal analysis demonstrated that RDE has stronger robustness, more accurate entropy estimation in noisy environments, has higher stability and confirms its computational efficiency. Three sets of real-world signal cases from different datasets show that RCHIMRDE method delivers the highest diagnostic measurement accuracy, outperforming existing entropy-based metrics.

Autonomous underwater vehicles (AUVs) are subject to various sources of faults during their missions, which challenges AUV control and operation in real environments. This paper addresses fault-tolerant trajectory tracking of autonomous underwater vehicles (AUVs) under thruster failures. We propose an adaptive Lyapunov-constrained model predictive control (LMPC) that guarantees stable trajectory tracking when the AUV switches between fault and normal modes. Particularly, we model different AUV thruster faults and build online failure identification based on Bayesian approach. This facilitates a soft switch between AUV status, and the identified and updated AUV failure model feeds LMPC controller for the control law derivation. The Lyapunov constrain in LMPC ensures that the trajectory tracking control remains stable during AUV status shifts, thus mitigating severe and fatal fluctuations when an AUV thruster occurs or recovers. We conduct numerical simulations on a four-thruster planar AUV using the proposed approach. The results demonstrate smooth transitions between thruster failure types and low trajectory tracking errors compared with the benchmark adaptive MPC and backstepping control with rapid failure identification and failure accommodation during the trajectory tracking.

This paper presents a fault-tolerant control for the trajectory tracking of autonomous underwater vehicles (AUVs) against thruster failures. We formulate faults in AUV thrusters as discrete switching events during a UAV mission, and develop a soft-switching approach in facilitating shift of control strategies across fault scenarios. We mathematically define AUV thruster fault scenarios, and develop the fault-tolerant control that captures the fault scenario via Bayesian approach. Particularly, when the AUV fault type switches from one to another, the developed control captures the fault states and maintains the control by a linear quadratic tracking controller. With the captured fault states by Bayesian approach, we derive the control law by aggregating the control outputs for individual fault scenarios weighted by their Bayesian posterior probability. The developed fault-tolerant control works in an adaptive way and guarantees soft-switching across fault scenarios, and requires no complicated fault detection dedicated to different type of faults. The entailed soft-switching ensures stable AUV trajectory tracking when fault type shifts, which otherwise leads to reduced control under hard-switching control strategies. We conduct numerical simulations with diverse AUV thruster fault settings. The results demonstrate that the proposed control can provide smooth transition across thruster failures, and effectively sustain AUV trajectory tracking control in case of thruster failures and failure shifts.

Dispersion entropy (DE) and its multiscale version multiscale DE (MDE) are effective measures of time series complexity. However, DE ignores skewness information, which can lead to inaccurate entropy estimates and is sensitive to the choice of category number, whereas MDE suffers from loss of information as the scale factor increases. To address the problems in DE, adaptive DE (ADE) is proposed, which selects the appropriate mapping method according to the skewness of the signal, and to solve the parameter selection problem by evaluating the complexity and data fitting goodness through the Akaike information criterion (AIC). In addition, Hermite interpolation multiscale ADE (HIMADE) is proposed by introducing the Hermite interpolation technique to approximately reconstruct the coarse-grained series to the original series, which solves the issue of information loss in MDE. Five sets of simulation signal analysis verify that the ADE is more sensitive to dynamic changes and has greater consistency, and HIMADE has greater stability, differentiation ability and noise immunity. Three sets of real-world signal cases from different datasets show that HIMADE has the highest accuracy in feature extraction compared with existing entropy metrics.