This paper presents a modular method for generating reference signals online for saturable synchronous machine drives. The method dynamically generates optimal references without precomputed lookup tables, following the maximum-torque-per-ampere (MTPA) trajectory while respecting maximum-torque-per-volt (MTPV), current, and voltage limits. The proposed tracking laws are formulated to yield exact, decoupled first-order error dynamics, ensuring predictable tracking responses and simplifying system tuning. The algorithm requires only the forward flux map, thereby eliminating the need for current-map inversion. By operating in a feedforward manner, the method ensures noise-free reference signals and structural separation from the feedback control. Both simulation and experimental results are presented, demonstrating that the proposed method achieves dynamic and steady-state performance on par with conventional lookup-table-based approaches, while avoiding the need for precomputed reference tables.
Rapidly shifting operational scenarios driven by uncertain Distributed Energy Resource (DER) profiles render conventional distribution network optimization methods either computationally expensive or poorly generalizable. This paper introduces GridRAG, a pioneering retrieval-augm…
This paper studies safe optimal output agreement for nonlinear multi-agent systems with output safety constraints. Existing safe feedback optimization methods often implement gradient-flow dynamics directly through the plant input, which may require high-order control barrier fun…
Interturn short circuits are among the most critical faults in permanent magnet synchronous motor drives, as they combine localized heating in the shorted stator phase with electrical asymmetry that distorts the current feedback used for torque-producing control. This article pro…
Accurate modeling and simulation are essential for the effective design, testing, and evaluation of electric machine systems. However, existing models often face interoperability challenges due to differences in programming languages (e.g., C, MATLAB, Python) and the separation o…
This paper develops a data-enabled primal-dual framework for learning optimal control policies for unknown linear discrete-time systems from online data. The proposed approach views the data-dependent control synthesis problem as a time-varying semidefinite program (SDP) whose co…
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable…