Design and Simulation of ANFIS Controller for Increasing the Accuracy of Leaf Spring Test Bench

Artificial Intelligence (AI) has been in use in several research fields and industries, including automotive. Intelligent control is a control technique that use different AI approaches like genetic algorithm, machine learning, neural networks and fuzzy logic. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the correlation of the experiment and simulation results of a 5 degrees of freedom (DOF) servo-hydraulic leaf spring test bench. A multi-body simulation (MBS) software named Simpack is used to model the actual leaf spring test bench in the simulation environment. However, the results of the simulations do not fully correlate with the results of experiments for the same scenarios. Simpack is a dynamic analyses software and is not a control design tool, but it has an interface that exchanges data with Matlab simultaneously. Therefore, Matlab/Simulink, with its powerful controller design toolboxes has been used for co-simulation with Simpack. ANFIS toolbox has been used to improve the simulation model within the Simpack. In this study, the power of the libraries of both Simpack and Matlab/Simulink are combined to perform better simulations. First, MBS model of test bench is improved by using the experimental data. Second, Sugeno type ANFIS with grid partitioning is designed by training different experimental datasets. The objective of the training is to evaluate the piston forces that correspond to the actual displacement. Lastly, the trained ANFIS model is implemented to the MBS model and co-simulations are performed. The results showed that the simulation results were better suited to experimental data when ANFIS was used. Piston performances are improved by 88,5%, 74,3%, 73,7% for piston 1, 2 and 3 respectively.