A multi-objective optimization method based on NSGA-II algorithm and entropy weighted TOPSIS for fuzzy active seat suspension of articulated truck semi-trailer

Abstract

This paper aims to optimize a fuzzy logic controller (FLC) active seat suspension applied to an articulated truck semi-trailer seat to improve ride comfort considering the energy consumption of the controller. The proposed truck model is a linear truck with 13 degree-of-freedom (DOF). Two objective functions are defined seat root mean square (RMS) acceleration related to ride comfort and controller RMS force pertaining to the energy consumption of the controller. The Pareto Front is obtained for these two objective functions using the multi-objective optimization method in MATLAB. The optimization is based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II), which has been proposed as a powerful decision space exploration engine based on a genetic algorithm (GA) for solving a multi-objective function problem. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is simple and effective, selects a set of optimal controller parameters. In addition, considering the changes of effective parameters in the truck, including trailer load and location, tyres’ stiffness and the driver’s mass, has been investigated to provide Monte Carlo sensitivity analysis of these parameters on objective functions. Finally, using ISO 2631-1, the ride comfort and controller required force levels before and after optimization are compared. The results of this optimization indicate a significant improvement in ride comfort and controller force which has been different in various conditions of truck speed and road classes. The results have shown that the maximum amount of improvement in ride comfort is about 25% which happens on Class-C road (at the truck speed of 60 km/h), and the control force reduction peaks at around 60%, which occurs on Class-A road (at the truck speed of 60 km/h). The simulation is validated by MSC-ADAMS software.