A Variable Self-Tuning Horizon Mechanism for Generalized Dynamic Predictive Control on DC/DC Boost Converters Feeding CPLs
Chuanlin Zhang, Mingdi Li, Liwen Zhou, Chenggang Cui, Long Xu
Abstract
In high-power electronic industrial systems, constant power loads (CPLs) can affect the system performance or even cause instability due to its inherent negative impedance characteristics. Regarding this control issue, model predictive control (MPC) methods are generally applied to dc/dc converters feeding CPLs, aiming to achieve satisfactory control performance. However, they are mostly based on a fixed horizon design, whose optimal performance may be far from the desired one due to the change of operating conditions, e.g., different variation levels of CPLs. In this context, a novel generalized dynamic predictive control (GDPC) strategy employing a simple self-tuning horizon mechanism is proposed for the first time, allowing the controller to adaptively optimize the system transient-time performance even under largely-varied operating conditions. First, a disturbance observer is utilized to reconstruct the lumped disturbances within the system, which are subsequently brought into the control design through feedforward compensation loops. Second, an adaptive horizon is introduced into the generalized predictive control (GPC) design and rigorous stability analysis based on Lyapunov theorem is given. The effectiveness and performance improvement of the proposed regulation methodology are verified by both simulation and experimental studies in reference to the double-loop proportional-integral (PI) control and a benchmark GPC algorithm.