Nonlinear PID-LQR controller based ACO for enhancing dynamics of active suspension systems

Abstract

This paper presents an innovative approach to analyzing and optimizing the performance of nonlinear hydraulic actuators for vehicle suspension systems. It focuses on dynamic behavior modeling and advanced control strategies designed to improve both driver comfort and vehicle performance. The study uses a comprehensive set of differential equations to model hydraulic operations, incorporating state-of-the-art control methodologies such as nonlinear PID (NL-PID) and advanced techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO). The novelty of this work lies in the development and application of the NL-PID-ACO control strategy, which shows superior performance in minimizing chassis displacement and sprung mass acceleration. Specifically, the NL-PID-ACO reduced displacement to 0.0126 m, representing a 35.38% improvement over the NL-PID-PSO, and achieved an optimal acceleration of 0.0632 m/s², a reduction of 90.6% compared to passive systems. On average, the NL-PID-ACO achieved a displacement of 0.0016 m and an acceleration of 0.078 m/s², corresponding to a 63.38% improvement over passive suspension systems. These findings underscore the unprecedented efficiency of the NL-PID-ACO control strategy, highlighting its potential as a transformative solution for optimizing the performance of nonlinear hydraulic actuators, ultimately contributing to enhanced driver comfort and vehicle dynamics.