TY - GEN
T1 - Auto-tuned model parameters in predictive control of power electronics converters
AU - Easley, Mitchell
AU - Fard, Amin Y.
AU - Fateh, Fariba
AU - Shadmand, Mohammad B.
AU - Abu-Rub, Haitham
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - Model predictive controlled (MPC) power electronics converters (PECs) features numerous benefits including fast transient response, multi-objective control in single-loop, and inclusion of constraints and nonlinearities in a simple manner. However, the control approach requires the accurate knowledge of the model parameters of the system. Potential model parameter mismatches with the actual values will result in performance deterioration of the MPC. Various agents like aging and harsh environmental conditions would yield changes in circuit impedance. This paper proposes an autonomous MPC for PECs subject to the variations and uncertainties in input filter impedance. The proposed controller adaptively tunes the model parameters used in the predictive equations, alleviating mismatch among the predictive model parameters and the actual system. The proposed auto-tuned modeling scheme requires no additional sensors while maintaining the simplicity of the controller. The proposed method is applicable to all types of PECs and filter topologies. The theoretical analysis and results demonstrate that the proposed approach embedded in the MPC makes the control algorithm robust to variations of model parameters of the system.
AB - Model predictive controlled (MPC) power electronics converters (PECs) features numerous benefits including fast transient response, multi-objective control in single-loop, and inclusion of constraints and nonlinearities in a simple manner. However, the control approach requires the accurate knowledge of the model parameters of the system. Potential model parameter mismatches with the actual values will result in performance deterioration of the MPC. Various agents like aging and harsh environmental conditions would yield changes in circuit impedance. This paper proposes an autonomous MPC for PECs subject to the variations and uncertainties in input filter impedance. The proposed controller adaptively tunes the model parameters used in the predictive equations, alleviating mismatch among the predictive model parameters and the actual system. The proposed auto-tuned modeling scheme requires no additional sensors while maintaining the simplicity of the controller. The proposed method is applicable to all types of PECs and filter topologies. The theoretical analysis and results demonstrate that the proposed approach embedded in the MPC makes the control algorithm robust to variations of model parameters of the system.
KW - Adaptive control
KW - Autonomous predictive control
KW - Model parameter mismatch
KW - Model predictive control
UR - http://www.scopus.com/inward/record.url?scp=85076750613&partnerID=8YFLogxK
U2 - 10.1109/ECCE.2019.8912881
DO - 10.1109/ECCE.2019.8912881
M3 - Conference contribution
AN - SCOPUS:85076750613
T3 - 2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019
SP - 3703
EP - 3709
BT - 2019 IEEE Energy Conversion Congress and Exposition, ECCE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2019
Y2 - 29 September 2019 through 3 October 2019
ER -