Three-phase AC-DC rectifiers are fundamental components in modern power electronics systems, yet achieving rapid voltage regulation and precise current tracking under load and grid disturbances remains challenging due to nonlinear dynamics and measurement uncertainties. This paper presents a finite-time control method for three-phase AC-DC rectifiers that achieves millisecond-scale regulation of DC-link voltage and grid currents under varying conditions. The proposed design employs a transformed augmented error-state dynamics model, extending the voltage dynamics to a two-state system to construct an adaptive sliding surface that guarantees fast finite-time convergence. A nonlinear sliding-mode voltage regulator with an online disturbance estimator ensures rapid and robust voltage tracking, while a fast current controller achieves finite-time dq-axis current tracking with minimal chattering. Theoretical results establish finite-time stability and provide explicit gain selection conditions. Simulation results demonstrate up to 99.40 per cent and 87.5 per cent reductions in voltage and current convergence times, respectively, compared to conventional robust controllers. Laboratory experiments further validate the approach, showing 33.33 per cent lower voltage ripple, 33.33 per cent faster rise time, and 32.43 per cent reduced steady-state error relative to a recent method. These results confirm improvements in transient performance, convergence, and overall system stability, highlighting the method's practical applicability for high-performance rectifier control.
Robust adaptive state-feedback control of linear time-varying systems under both potentially unbounded system's modeling uncertainty and external disturbance
Adaptive stabilization of a leaderless bearing-constrained formation with disturbances
East Asia University of Technology（东亚理工大学）；University of Southern California（南加州大学）；Ghost Robotics（幽灵机器人公司）；Hanoi University of Science and Technology（河内科技大学）
Adaptive MPPI with Online Disturbance Covariance Estimation: Provable Stability Tightening via Spatial Smoothing
Department of Mechanical and Nuclear Engineering, Tennessee Technological University（机械与核工程系，田纳西技术大学）；School of Engineering, Department of Electrical and Computer Engineering, Mercer University（工程学院，电气与计算机工程系，梅尔基大学）
Deep Reinforcement Learning Optimization for Uncertain Nonlinear Systems via Event-Triggered Robust Adaptive Dynamic Programming
Department of Computer Science, University College London（计算机科学系，伦敦大学学院）；Department of Electrical and Electronic Engineering, University of Manchester（电子与电气工程系，曼彻斯特大学）
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Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Japan（国家先进工业科学与技术研究院智能系统研究所，日本）；Graduate School of Systems and Information Engineering, University of Tsukuba, Japan（茨大大学系统与信息工程研究生院，日本）
