%0 Journal Article %1 MustefaJibril12020d %A Mustefa Jibril 1, Eliyas Alemayehu Tadese 2 %D 2020 %J Report and Opinion Journal %K Active NARMA-L2 controller controller, model predictive reference suspension system, %N 6 %P 7-13 %R 10.7537/marsroj120620.02 %T Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System %V 12 %X Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valvepiston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system.

@article{MustefaJibril12020d, abstract = {Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valvepiston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system.}, added-at = {2020-09-01T13:57:51.000+0200}, author = {Mustefa Jibril 1, Eliyas Alemayehu Tadese 2}, biburl = {https://www.bibsonomy.org/bibtex/2fda0b2db6060d6b5b8253aacfecf0f67/mustefa1981}, doi = {10.7537/marsroj120620.02}, file = {:C\:\\Users\\user\\Desktop\\NEW JOURNAL FOR ORCID\\Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System.pdf:PDF}, interhash = {2eb0d68b8dcf9adf07ae1923fceb289a}, intrahash = {fda0b2db6060d6b5b8253aacfecf0f67}, journal = {Report and Opinion Journal}, keywords = {Active NARMA-L2 controller controller, model predictive reference suspension system,}, month = {June}, number = 6, owner = {user}, pages = {7-13}, review = {peer reviewed}, timestamp = {2020-09-01T13:58:22.000+0200}, title = {Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear Quarter Car Active Suspension System}, volume = 12, year = 2020 }