Optimized Genetic Algorithms Reduced Order Model Based RST Roll Control of Antiroll Bar Dedicated to Semi-active Suspension

Abstract

Working with high-order transfer functions needs a lot of work and leads to major difficulties in analysis, simulation, and control design. Model reduction studies the large-scale system properties and helps to reduce these difficulties. In this paper, the genetic algorithms (GA) optimization method is used to calculate the second reduced order model (ROM) of the original high order model (HOM) of the actuator. Here, the studied hydraulic actuator is a single input, single output (SISO), and linear time invariant (LTI) system that can be modeled by an eight-order transfer function with uncontrollable modes. The genetic algorithms are successfully applied to reduce the original model order using MATLAB software. Thus, the proposed approach is applied to both the original and suggested reduced order models to check the effectiveness of the reduction method. Finally, a digital RST roll control based on the robust pole placement is applied for the two models, and simulations are carried out to show the effectiveness of the control strategy