A Real-Time Offset-Free Neural Network MPC Framework for Aeroengine Control on Embedded Systems

Model predictive control (MPC) for aeroengines requires accurate prediction of complex nonlinear dynamics, which is challenging to achieve using traditional modeling approaches. While neural networks (NNs) offer strong nonlinear approximation capability, their embedded deployment suffers from approximation and quantization errors, leading to persistent steady-state offsets in closed-loop control. To address these limitations, this article proposes a real-time offset-free MPC framework that incorporates an NN-based prediction model with a multivariable adaptive error compensator to eliminate steady-state deviations and enhance tracking performance. Moreover, to ensure real-time feasibility, a low-rank approximation linearization method is developed to reduce computational complexity, and the entire framework is hardware-accelerated on a Zynq deep-learning processing unit (DPU) for embedded execution. Finally, hardware-in-the-loop (HIL) experiments on realistic aeroengine control scenarios demonstrate that the proposed approach achieves a faster dynamic response, improved steady-state accuracy, and a lower computation latency compared with conventional MPC, confirming its potential for practical aerospace applications.