Abstract
Abstract This paper presents two methods of creating model predictive control strategies for efficient real-time control algorithms for power electronics. Two novel methods of performing nonlinear modeling are presented in this research, the first being novel Takagi-Sugeno model, which combines two linear state space models using a membership function to model the nonlinear transitions between operating points. The second method involves using sparse identification of nonlinear dynamics, a nonlinear modeling technique that uses L1 regularization of least squares on for time series data to define a parsimonious polynomial function set. This set is used to define the input feature space to extended dynamic mode decomposition with control. These models are then used for data-driven model predictive control of a buck switch mode power supply to find the optimal duty cycle that regulates the output voltage over a finite receding horizon and validated using an LTSPICE simulation to assess their effectiveness in comparison to a tuned proportional integral derivative controller. Numerical accuracy challenges are discussed, and strategies are offered for their mitigation.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call