<![CDATA[Induction motor (IM) drives often suffer performance degradation under load variations and parameter uncertainties when using conventional proportional–integral (PI)- based field-oriented control (FOC). To address these issues, this study proposes a composite control framework combining a PI regulator in the speed loop with a Lyapunov-based variable-gain super twisting algorithm (VGSTA) for the inner current loops to enhance robustness against disturbances and parameter variations. In addition, a load torque observer is developed to estimate unknown disturbances in real time and generate an equivalent compensation current, thereby improving disturbance rejection. Unlike existing approaches, the proposed strategy achieves a balance between simplicity, robustness, and smooth control by integrating classical PI control with higher-order sliding mode techniques and adaptive observer dynamics. Furthermore, the controller and observer gains are optimized using particle swarm optimization (PSO) to improve convergence and reduce overshoot under uncertain conditions. Simulation results demonstrate accurate speed regulation, effective chattering reduction, and reliable operation under load and parameter variations. Due to its low computational complexity and high robustness, the proposed method is well suited for industrial drive systems and electric mobility applications.]]>
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