Passivity-Based Inverse Model Predictive Control with Reduced Sensors for Grid-Forming Inverters


Sharida A., Kouzou A., Karaki A., FESLİ U., Bayhan S., Abu-Rub H.

IEEE Transactions on Power Electronics, vol.41, no.4, pp.6196-6206, 2026 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 41 Issue: 4
  • Publication Date: 2026
  • Doi Number: 10.1109/tpel.2025.3626253
  • Journal Name: IEEE Transactions on Power Electronics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.6196-6206
  • Keywords: Grid forming inverter, inverse model predictive control (IMPC), passivity-based control, sensorless control
  • Gazi University Affiliated: Yes

Abstract

This paper proposes a novel control strategy for grid-forming (GFM) inverters based on passivity principles and inverse model predictive control (IMPC). The principle of passivity-based control is utilized to reduce the number of required measurements, which enhances the reliability, simplifies the design, and reduces the cost of the control system. Moreover, the proposed approach is generalized to operate reliably even when any group of sensors is unavailable, provided that the other three sensor groups remain available. The sensor groups typically include inverter-side current sensors, filter capacitor voltage sensors, grid-side current sensors, and grid-side voltage sensors. Furthermore, the IMPC technique is employed to control the inverter states effectively, with minimal computational overhead. A key advantage of this approach lies in its ability to generate optimal control signals with a reduced number of sensors, which makes IMPC both optimal and cost-effective. The proposed controller is experimentally validated in multi-distributed generation (DG) scenarios and is benchmarked against the conventional MPC to demonstrate its effectiveness.