Energy-Aware FPGA Implementation of an Adaptive NTSMC–RBFNN Controller for PMSM EV Traction

Permanent magnet synchronous motors (PMSMs) are widely used in electric vehicle (EV) traction, where speed control must be accurate, robust, and executable in real time on resource-limited embedded hardware. This paper presents an adaptive nonsingular terminal sliding-mode controller combined with a radial basis function neural network (NTSMC–RBFNN), in which the network estimates and compensates the lumped disturbance online. Stability is analyzed in continuous time under stated assumptions and, for the sampled-data implementation, only in a conditional, practical sense. The controller is reformulated in fixed-point arithmetic and deployed on a Xilinx Zynq-7000 FPGA through a parallel, pipelined architecture. In FPGA-in-the-loop (FIL) tests, it achieves a speed-tracking RMSE of 0.072 rad/s and recovers from a step load change within 4 ms, and it remains stable under ±50% variations in rotor inertia and flux linkage. One control update takes 3.2 µs (0.32% of the 1 ms sampling period), uses less than 6% of the device resources, and consumes an estimated 1.02 µJ of dynamic energy (Vivado post-implementation estimate). In an ablation study, removing the RBFNN estimate multiplies the load-step RMSE by 3.7, and replacing the nonsingular terminal surface by a conventional one increases the tracking RMSE by 75%. A sensitivity study identifies Nh = 7 hidden neurons as the retained compromise between accuracy, resources, and energy. The plant is simulated and the power figures are estimates; experimental validation on a physical drive is left for future work.