An Efficient Long Prediction Horizon Design for Generalized Predictive Control of DC/DC Boost Converter

Generalized predictive control (GPC) stands out as a prominent representative in the predictive control family due to its robustness. As a crucial parameter in the GPC controller, the prediction horizon significantly impacts the control performance. Short prediction horizons may result in instability, whereas long prediction horizons can lead to a high computational burden but better stability. Although the conventional design approaches can be realized by empirical prediction horizon selection or incorporating nonlinear observers, a theoretical approach has been overlooked so far. To bridge this gap, this article introduces a novel approach to design prediction horizons for GPC demonstrated on a dc/dc boost converter. This method involves constructing a closed-loop system model and assessing the impact of different prediction horizons on the system. Based on the system modeling, a rigorous boundary for the prediction horizon is obtained to ensure control stability. Finally, the accuracy of the design method has been confirmed in experimental conditions. Notably, beyond establishing a rigorous prediction horizon boundary, the proposed method demonstrates a reduction in computational time by at least 22% compared with the empirically selected prediction horizons within the studied system.