An Optimization Framework for Controller Tuning of Higher Order Linear Time Invariant Systems Using Genetic Algorithms

In this paper, we present a simple and effective strategy for designing controllers for large-scale linear time-invariant systems using Genetic Algorithms (GA). Instead of relying on traditional, complex controller structures, more weightage is given to reduced-order models that capture the essential dynamics of large, complex systems, making control design implementation easier. GA is used to automatically tune controller parameters, thereby ensuring optimal performance regardless of the system type or order. Simulation results exhibit consistent improvements, including smaller rise and settling times, zero overshoot, and excellent stability characteristics, irrespective of the model reduction technique employed. The proposed approach to controller design combines simplicity with high performance, providing control engineers with a practical and reliable tool to address control challenges in higher-order systems.