Abstract
Predictive current control (PCC) applied on permanent magnet synchronous motors (PMSMs) has been developed into mainly three methods: the conventional finite-control-set PCC, the double voltage vectors PCC, and deadbeat PCC. However, each approach has its particular calculation way for voltage vectors selection and respective execution duration. This paper, based on the deadbeat idea, presents a unified predictive current control scheme of PMSMs. Under this scheme, the prior three classes are able to be clearly unified into one frame with lower calculation effort. Furthermore, to cope with problem of parameter mismatch in dq-axis current predictive model, a integrated identification method is proposed. Firstly, data selectors are designed to reject abnormal data of sampling signals, and then the interval-varying multi-innovation least squares algorithm is combined with forgetting factor (V-FF-MILS) to approximate the error terms caused by electromagnetic parameters error. The estimated results are online fed to the model of PMSM to enhance its accuracy. Finally, the processor in loop (PIL) simulation results verify that the proposed integrated scheme has advantages in current control of PMSMs with large-scale parameter uncertainty.
Highlights
Owing to its advantages on efficiency, power density, and compact structure, permanent magnet synchronous motors (PMSMs) have been widely employed in various electrical drive appliances in vehicle, aerospace, and other industrial fields
This paper proposes a generalized predictive current control scheme based on the principle of deadbeat control and vector composition in an explicit way
The results show that the performance of proposed 1-predictive current control (PCC) is not inferior to the conventional PCC
Summary
Owing to its advantages on efficiency, power density, and compact structure, PMSMs have been widely employed in various electrical drive appliances in vehicle, aerospace, and other industrial fields. The Deadbeat PCC is similar to the FOC scheme, which usually has a space vector PWM modulator, and its optimal control set composed by two adjacent active VVs and a null VV is determined by the reference voltage, which is directly obtained from the predictive model of PMSMs based on the deadbeat idea [3], while that of FOC is from a specific PI or other complex current controller. Advanced robust techniques are often introduced to design control law, such as the control-Lyapunov function [13], disturbance estimation [14], and differential flatness [15] Another strategy lies in the parameter identification, such as a recursive inductance estimator [9], multi-parameter identification with a decoupling method [16], model reference adaptive technique [17], extended Kalman filter [18], modified particle swarm optimization [19], Adaline neural network [20], etc.
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