Multi-Objective Optimization Based Feedback Gains Design of Adaptive Full-Order Observer for Induction Motor Sensorless Drive

This paper focuses on the feedback gains design methods of induction motor sensorless drive for stability enhancement in low-speed regenerating region. It is quite difficult to select legitimate values of feedback gains since the four coefficients are coupling. The existing methods are limited to designing feedback gains artificially, and only one design objective is considered in each paper. To address this issue, a multi-objective optimization based (MOB) feedback gains design of adaptive full-order observer for induction motor sensorless drive is developed. Three objective functions are developed based on the robustness analysis. Then the MOB method combines these optimal functions to enhance the speed observation stability, the flux and speed estimate precision together in the low-speed regenerating region. The second generation of non-dominated sorting genetic algorithms (NSGA-II) is used to get the possible optimal solutions. Using an elite approach prevents the evolutionary process from excluding specific members of the best populations, the NSGA-II increases the precision of the optimization findings. Finally, the suggested feedback gains from the optimal solutions are used in experiments. The effectiveness of the proposed method is verified by a 2.2 kW induction motor experimental setup.