TY - JOUR
T1 - Neural Predictor-Based Dynamic Surface Predictive Control for Power Converters
AU - Liu, Xing
AU - Qiu, Lin
AU - Rodriguez, Jose
AU - Wu, Wenjie
AU - Ma, Jien
AU - Peng, Zhouhua
AU - Wang, Dan
AU - Fang, Youtong
N1 - Publisher Copyright:
IEEE
PY - 2022
Y1 - 2022
N2 - In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of ``explosion of complexity" inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter.
AB - In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of ``explosion of complexity" inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter.
KW - Complexity theory
KW - dynamic surface control
KW - Explosions
KW - Neural networks
KW - Predictive control
KW - predictor-based neural network
KW - Proposals
KW - Robustness
KW - Uncertainty
KW - weighting factors
UR - http://www.scopus.com/inward/record.url?scp=85124187512&partnerID=8YFLogxK
U2 - 10.1109/TIE.2022.3146643
DO - 10.1109/TIE.2022.3146643
M3 - Article
AN - SCOPUS:85124187512
SN - 0278-0046
VL - 70
SP - 1057
EP - 1065
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 1
ER -