Horizon-one model-predictive control with stability guarantee for a permanent magnet synchronous motor

This paper proposes a horizon-one nonlinear model predictive control (MPC) strategy for permanent magnet synchronous motors that combines formal stability guarantees with real-time implementability. The controller is derived directly from the original bilinear discrete-time model, avoiding linear approximations commonly used in motor-drive applications. Despite the nonlinear formulation, the associated optimal control problem admits a closed-form analytical solution with a unique global optimum, enabling efficient implementation on resource-constrained microcontrollers. Adaptivity is achieved through a recursive nonlinear identification scheme and a disturbance observer, which allow online estimation of uncertain parameters and mechanical torque without direct torque measurements. Closed-loop stability is established using discrete-time Lyapunov theory, and sufficient conditions for asymptotic stability and region-of-attraction estimation are provided. Simulation and experimental results demonstrate that the proposed approach achieves improved transient performance and reduced overshoot compared to conventional field-oriented control, while maintaining low computational cost and robust operation under disturbances.