PID Parameters Prediction Using Neural Network for A Linear Quarter Car Suspension Control
Journal Title: International Journal of Intelligent Systems and Applications in Engineering - Year 2016, Vol 4, Issue 1
Abstract
Providing control for suspension systems in vehicles is an enhancing factor for comfort and safety. With the improvement of control conditions, it is possible to design a cost-efficient controller which will maintain optimum comfort within harsher environmental conditions. The aim of this study is to design an adaptive PID controller with a predictive neural network model, which will be referred as NPID (NeuralPID), to control a suspension system. For this purpose, a NN (Neural Network) model is designed to produce outputs for PID’s Proportional (P) parameter to provide optimum responses for different road inputs. Also, reliability of the system outputs, which is using adaptive Proportional parameter, is tested. PID parameters for linear quarter vehicle model are decided through Zeigler-Nichols method. An ideal PID model, where Integral (I) and Derivative (D) parameters are bound to Proportional parameter, is used in the system. When the outputs of different controlled and not controlled systems, which are free, PID and NPID, are compared; it has been seen that NPID outputs are more convenient. In addition, it is possible to design controllers, with adaptively adjusting P parameter, which are operating cost-effective.
Authors and Affiliations
Kenan Muderrisoglu| Yıldız Technical University – Mechatronic Engineering Department, Dogan Onur Arisoy| Boğaziçi University – Institute of Biomedical Engineering, A. Oguzhan Ahan| Yıldız Technical University – Mechanical Engineering Department, Erhan Akdogan| Yıldız Technical University – Mechatronic Engineering Department
Keywords
Related Articles
- EP ID EP793
- DOI 10.18201/ijisae.75361
- Views 454
- Downloads 27
Validation of Registration for Renal Dynamic Contrast Enhanced MRI Imaging
In Dynamic Contrast Enhanced Resonance Imaging (DCE-MRI), abdomen is scanned repeatedly and rapidly after injection of a contrast agent. During data acquisition, collected images suffer from the motion induced by the pat...
Particle Swarm Optimization Based Approach for Location Area Planning in Cellular Networks
Location area planning problem plays an important role in cellular networks because of the trade-off caused by paging and registration signalling (i.e., location update). Compromising between the location update and the...
Grade prediction improved by regular and maximal association rules
In this paper we propose a method of predicting student scholar performance using the power of regular and maximal association rules. Due to the large number of generated rules, traditional data mining algorithms can bec...
A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index
In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. Here the proposed model is utilized to predict the chaotic activity of the earth’s magnetosphere....
Design and Implementation of High Speed Artificial Neural Network Based Sprott 94 S System on FPGA
FPGA-based embedding system designs have been preferred for industrial applications and prototyping because of the advantages of parallel processing, reconfigurability and low cost. Due to having characteristic structure...