LQR-Based State Feedback for PMSM with Enhanced Dynamics via Integral Extended Modeling

https://doi.org/10.58571/CNCA.AMCA.2025.070

Resumen: This work proposes a novel state-feedback controller for Permanent Magnet Synchronous Motors (PMSMs) based on an extended integral model with dynamic input. Traditional Field-Oriented Control (FOC) schemes use cascade PI loops to regulate torque and speed; however, their dynamic response is limited by saturation effects. To overcome this, we design an optimal controller using the Linear Quadratic Regulator (LQR) theory, which employs an augmented state-space model that integrates tracking errors and replaces the inner current loop with a dynamic input. The Riccati equation is solved for customized matrices to minimize control energy while ensuring a fast transient response. Simulations under abrupt speed changes (from negative to positive) and constant load torque demonstrate the controller’s superiority over conventional PI-FOC.

¿Cómo citar?

Aldrete-Maldonado, Ch., Coria, L., Ramírez-Villalobos, R. & Osorio-Gordillo, G. (2025). LQR-Based State Feedback for PMSM with Enhanced Dynamics via Integral Extended Modeling. Memorias del Congreso Nacional de Control Automático 2025, pp. 409-414. https://doi.org/10.58571/CNCA.AMCA.2025.070

Palabras clave

PMSM, optimal control, PI control, Model extended, dynamic input.

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