A Neural-Network-Based Electric Machine Emulator Using Neuro-Fuzzy Controller for Power-Hardware-in-the-Loop Testing

The emulation of permanent magnet synchronous machines (PMSMs) is critical for the advancement of power electronics and drive converter testing, particularly within power-hardware-in-the-loop (PHIL) platform. Despite significant progress, and developing accurate machine models, the amount of resources and memory used by these accurate models are not ideal for real-time applications due to added latency. Hence, a research gap exists in developing models that while accurately and efficiently replicate the dynamic behaviors of the machine model under various operating conditions, are light in resource usage. This paper addresses this gap by introducing an artificial neural network (ANN)-based machine modeling approach and combines it with a neuro-fuzzy-based control strategy to ensure robust and precise performance of the system, that is to minimize the error between the electric machine emulators (EME) and physical PMSM test results. The ANN model requires only 0.68 KB of memory compared to the 4 MB needed for traditional 1,000 × 1,000 LUT-based models, which incur greater latency due to cache limitations and interpolation demands despite lower floating-point operation (FLOP) requirements. By using this optimized ANN model with an adaptive ANFIS controller, the proposed system So, the main objective is to enhance the performance and accuracy of EMEs in PHIL testing environments. The ANN model provides a resource-efficient yet precise representation of the PMSM, while the adaptive neuro-fuzzy inference system (ANFIS)-based controller dynamically adjusts its membership functions to adapt to changing system dynamics and loading conditions and provide proper control command.