Value Function Approximation for Direct Control of Switched Power Converters
For control engineers designing real-time controllers for power converters, this work offers a practical method to reduce online computation, though it is an incremental improvement over existing approximation techniques.
This paper addresses the computational bottleneck of model predictive control for switched power converters by proposing an offline-computed quadratic cost approximation that splits the planning horizon, enabling real-time control with adjustable computational load. Simulation results demonstrate the approach's effectiveness.
We consider the problem of controlling switched-mode power converters using model predictive control. Model predictive control requires solving optimization problems in real time, limiting its application to systems with small numbers of switches and a short horizon. We propose a technique for using off-line computation to approximate the model predictive controller. This is done by dividing the planning horizon into two segments, and using a quadratic function to approximate the optimal cost over the second segment. The approximate model predictive algorithm minimizes the true cost over the first segment, and the approximate cost over the second segment, allowing the user to adjust the computational requirements by changing the length of the first segment. We conclude with two simulated examples.