
doi: 10.1002/cta.70442
ABSTRACT For buck converters operating under variable load conditions, fast transient response characteristics and strong interference immunity are critical factors ensuring stable and reliable operation. To address the issues of response lag and insufficient interference immunity during load transients, this paper proposes a robust cascaded control scheme combining discrete‐time model predictive control (MPC) with an improved linear active disturbance rejection control (LADRC). To overcome the inherent bandwidth limitations and phase lag of conventional linear extended state observer (LESO), a differential feedforward path is introduced into the traditional LESO architecture. The improved LESO achieves faster load disturbance estimation while maintaining robust noise immunity. An efficient two‐step predictive MPC algorithm is employed in the inner loop to eliminate computational delays and ensure rapid current tracking. Rigorous discrete‐time stability analysis based on the Jury criterion and Lyapunov theory provides a theoretical foundation for parameter design. Experimental results validate that the proposed method significantly reduces voltage recovery time and overshoot under load transients, demonstrating superior robustness.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
