Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors
In this paper, an inductance surface estimation and learning for utilization with a stochastic model predictive control (MPC) scheme for the current control of switched reluctance motors (SRMs) is introduced. This MPC is equipped with state estimators and is implemented as a recursive linear quadratic regulator for practical deployments in hybrid vehicle applications. Additionally, a learning mechanism is developed to dynamically adapt to the inductance profile of the machine and update the MPC and Kalman filter parameters. The introduced control scheme can cope with noise as well as uncertainties within the machine nonlinear inductance surface. The introduced system will benefit from a fixed switching frequency and will offer low current ripples by calculating the optimal duty cycles using the SRM model. Finally, simulations and experimental results are provided to evaluate the proposed method.
X. Li and P. Shamsi, "Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors," IEEE Transactions on Transportation Electrification, vol. 1, no. 3, pp. 287-297, Institute of Electrical and Electronics Engineers (IEEE), Oct 2015.
The definitive version is available at https://doi.org/10.1109/TTE.2015.2468178
Electrical and Computer Engineering
Keywords and Phrases
Electric current control; Electric drives; Hybrid vehicles; Inductance; Kalman filters; Model predictive control; Predictive control systems; Stochastic control systems; Stochastic models; Stochastic systems; Traction motors; Delay compensation; Inductance estimations; Motor drive; Predictive control; Recursive least square (RLS); Surface learning; Switched reluctance; Reluctance motors; Current control; Inductance surface learning; LQR; MPC; SRM
International Standard Serial Number (ISSN)
Article - Journal
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Oct 2015