Robust nonlinear control of permanent magnet synchronous motor drives: An evolutionary algorithm optimized passivity-based control approach with a high-order sliding mode observer

Abstract

Permanent Magnet Synchronous Machines (PMSMs) have revolutionized motor design by replacing traditional components like rotor windings, brushes, and sliding contacts with permanent magnets. This innovation has significantly improved operational efficiency and reduced maintenance needs. However, controlling PMSMs remains challenging due to the changing dynamics of the machine over time and its sensitivity to different environmental conditions.

To tackle these challenges, this study presents a novel nonlinear control approach called passivity-based control (PBC). Unlike conventional methods, PBC manages both the electrical and mechanical dynamics of the system, focusing on energy flow and dissipation to maintain stability. To make the control more robust, the approach combines a nonlinear observer and a high-order sliding mode controller (HSMC), which enhance the system's ability to handle disturbances and parameter changes. Additionally, the study uses Genetic Algorithm (GA) optimization to fine-tune the parameters of the PBC, observer, and HSMC. This optimization improves the motor's tracking accuracy and robustness against external disruptions.

The result is a control framework that preserves the natural dynamics of PMSMs while improving their stability and performance. Experimental validation using the platform for real-time simulation (OPAL-RT) and real world on a PMSM using dSPACE DS1202 board demonstrates that this method outperforms existing techniques under a variety of operating conditions, highlighting its effectiveness and reliability.