A Novel Constrained Offset-Free Model Predictive Position Controller for PMSM Drives With a Computationally Efficient Design

This article proposes a novel computationally efficient constrained offset-free model predictive position controller (OFMPPC) for surface-mounted permanent magnet synchronous motor (SPMSM). The proposed controller follows a cascaded-free structure based on the system’s augmented state space model. Meanwhile, the observability is discussed and a Kalman filter is then incorporated for zero steady state error and robustness improvement. Further, the constraint inequalities are formulated considering all the speed, current and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-axis voltage constraints. And a novel constraint handling strategy is intuitively designed based on the reference governor (RG) theory, where specific modifications are made accordingly to deal with the PMSM application. The detailed design procedure is then given and the constrained OFMPPC with RG is constructed. The proposed method can handle all the constraints but significantly reduce the calculation burden compared with the conventional solver-based quadratic programming solving methods, making model predictive control applicable in real applications. Finally, experiments are conducted based on different scenarios to prove the feasibility and performance of the proposed method.