Robust Perturbation Observer-based Finite Control Set Model Predictive Current Control for SPMSM Considering Parameter Mismatch

Abstract

In order to improve the dynamics of the surface-mounted permanent magnet synchronous motors (SPMSM) used in servo systems, finite control set model predictive current control (FCS-MPCC) methods have been widely adopted. However, because the FCS-MPCC is a model-based strategy, its performance highly depends on the machine parameters, such as the winding resistance, inductance and flux linkage. Unfortunately, the parameter mismatch problem is common due to the measurement precision and environmental impacts (e.g., temperature). To enhance the robustness of the SPMSM FCS-MPCC systems, this paper proposes a Lundberg perturbation observer that is seldom used in the FCS model predictive control situations to remove the adverse effects caused by resistance and inductance mismatch. Firstly, the system model is established, and the FCS-MPCC mechanism is illustrated. Based on the machine model, the sensitivity of the control algorithm to the parameter mismatch is discussed. Then, the Luenberger perturbation observer that can estimate the general disturbance arising from the parameter uncertainties is developed, and the stability of the observer is analyzed by using the discrete pole assignment technique. Finally, the proposed disturbance observer is incorporated into the FCS-MPCC prediction plant model for real-time compensation. Both simulation and experiments are conducted on a three-phase SPMSM, verifying that the proposed strategy has marked control performance and strong robustness.

Low-Switching-Loss Finite Control Set Model Predictive Current Control for IMs Considering Rotor-Related Inductance Mismatch

“…In terms of the situation in which L r_off =1.4L r and L m_off =0.4L m , the current ripples also witness an obvious decrease when the observed inductances are employed for control. These comply with the conclusion in [15], meaning that the proposed SMO-based FCS-MPCC method has strong robustness against parameter uncertainties.…”

“…Step 6) Evaluation: Substitute the seven the predicted values into the cost function (15) and determine the optimal voltage vector and the corresponding switching states.…”

“…Step 6) Third-time prediction: Repeat step 4) to obtain i sM (k+1) and i sT (k+1). step 7) Evaluation: Use the cost function (15) to select the optimal switching states.…”

“…Finite control set (FCS) model predictive control (MPC), which is endowed with remarkable simplicity and strong constraint capability, has been widely used in different electric powertrains [11][12][13][14]. Without adopting any modulators for pulse width modulation (PWM) signal generation, FCS-MPC directly uses the output of the controller to determine the optimal switching state [15]. In comparison with the traditional modulator-based fieldoriented control (FOC) methods whose switching frequency is fixed and equals the control frequency, the switching frequency of an FCS-MPC method is uncertain and twice lower than the control frequency.…”

Research on Control Strategy of the Electric Power Steering System for All-Terrain Vehicles Based on Model Predictive Current Control

“…Using current as the variable, a prediction model based on the discrete state-space equations of the system is established, and a rolling optimization method is used to realize the current control of SPMSM. Without considering the parameter uncertainty, the electromagnetic model of the permanent magnet synchronous motor in the d-axis and qaxis coordinate system is According to equations (5) and (6), the continuous statespace equation of SPMSM is derived [33]:…”