Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

A model predictive control with online inductance estimator (OIEMPC) for three-phase VIENNA rectifiers is presented in this paper. An OIE adopting the dynamic filtering method and least square algorithm is proposed to further improve system robustness. In this OIE, the VIENNA rectifier pole voltages are obtained by computation utilizing the gate signals of the switching devices, AC current direction and the DC voltage, so that no additional voltage sensors are needed. Compared to the conventional MPC with finite control set (FCS-MPC), the proposed strategy can have better control performance, which effectively improves the robustness of the system. The effectiveness and correctness of the proposed OIEMPC method are demonstrated by the results of Simulink experiments.