Sparse Identification of Nonlinear Dynamics-Based Feature Extraction for Data Driven Model Predictive Control of a Buck Boost Switch Mode Power Supply

This paper presents two methods of creating model predictive control (MPC) 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 (SINDy), a nonlinear modeling technique that uses L1 regularization of least squares 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 (DMD) 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 tuned proportional integral derivative (PID) controller. Numerical accuracy challenges are discussed, and strategies are offered for their mitigation.