FPGA based adaptive Neuro Fuzzy Inference controller for full vehicle nonlinear active suspension systems

Aldair, Ammar A and Wang, Weiji (2010) FPGA based adaptive Neuro Fuzzy Inference controller for full vehicle nonlinear active suspension systems. International Journal of Artificial Intelligence and Applications, 1 (4). pp. 1-15. ISSN 0976-2191

[img] PDF - Published Version
Restricted to SRO admin only
Available under License Creative Commons Attribution.

Download (303kB)

Abstract

A Field Programmable Gate Array (FPGA) is proposed to build an Adaptive Neuro Fuzzy Inference System (ANFIS) for controlling a full vehicle nonlinear active suspension system. A Very High speed integrated circuit Hardware Description Language (VHDL) has been used to implement the proposed controller. An optimal Fraction Order PIlDμ (FOPID) controller is designed for a full vehicle nonlinear active suspension system. Evolutionary Algorithm (EA) has been applied to modify the five parameters of the FOPID controller (i.e. proportional constant Kp, integral constant Ki, derivative constant Kd, integral order l and derivative order μ). The data obtained from the FOPID controller are used as a reference to design the ANFIS model as a controller for the controlled system. A hybrid approach is introduced to train the ANFIS. A Matlab Program has been used to design and simulate the proposed controller. The ANFIS control parameters obtained from the Matlab program are used to write the VHDL codes. Hardware implementation of the FPGA is dependent on the configuration file obtained from the VHDL program. The experimental results have proved the efficiency and robustness of the hardware implementation for the proposed controller. It provides a novel technique to be used to design NF controller for full vehicle nonlinear active suspension systems with hydraulic actuators.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
Research Centres and Groups: Dynamics, Control and Vehicle Research Group
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ0212 Control engineering systems. Automatic machinery (General)
Depositing User: William Wang
Date Deposited: 04 Jan 2018 10:35
Last Modified: 04 Jan 2018 10:35
URI: http://sro.sussex.ac.uk/id/eprint/72594

View download statistics for this item

📧 Request an update