A systematic parametrization procedure for PID controller applied on quarter suspension system using the giant armadillo optimization algorithm

Abstract

Active suspension is an automotive technology that controls the vertical movements of the car wheels through an electronically controlled force actuation system. This kind of system deals with security and comfort of the driver and passengers simultaneously. Therefore, a digital controller is required to regulate the system dynamics properly. In the literature, proportional–integral controllers are often used or combined with other techniques to stabilize the car. However, obtaining the optimal gains from this kind of controller is highly dependent on the designer’s experience. In addition, there are some methods for determining the controller gains. However, these methods do not ensure optimal gains. Taking that into consideration, this work presents a systematic procedure for controller parametrization of an active suspension system using a meta-heuristic optimization algorithm. The optimizer is the giant armadillo optimization algorithm, which is bioinspired in the hunting behavior of giant armadillos that move towards prey positions and digging termite mounds. Numerical simulations indicate the feasibility of the proposed parametrization procedure and its superiority in relation to the Ziegler & Nichols method, providing a reduction of 36.36% and 39.34% of mean absolute error and root mean squared error, respectively.