@article{MAKHILLJEAS2019141818445,
    title = {Real-Time Adaptive Intelligent FPGA-based Back-Stepping Control Law Design for a
Nonlinear Magnetic Ball Levitation System},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {18},
    pages = {6912-6929},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.6912.6929},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.6912.6929},
    author = {Khulood E.},
    keywords = {LabVIEW package,MATLAB package,magnetic levitation system,integrated
software environment,FPGA-Kit,Intelligent Bat Optimization (IBO) algorithm,Adaptive back-stepping method},
    abstract = {A new proposal for an adaptive intelligent Field Programmable Gate Array (FPGA) back-stepping
nonlinear control law equation design and implementation for a strong nonlinear Magnetic ball Levitation
(MagLev) system is introduced in this study. The aim of the suggested adaptive controller is to retain and
stabilize the position of the magnetic ball to be suspended in a desired position within the magnetic field. The
on-line Intelligent Bat Optimization (IBO) algorithm is used to find and tune the positive gain variables of the
proposed adaptive control law based on Lyapunov method. These gain variables are used to obtain a suitable
voltage action for the nonlinear system. The adaptive back-stepping technique is implemented by using FPGA
Kit based on the schematic design of the Xilinx development tool Integrated Software Environment (ISE) with
a high level programming language which named the Verilog. This language is used for testing the on line
operation of the adaptive intelligent FPGA-Back-Stepping controller. The numerical simulation results along
with the experimental work illustrate the improved performance of the proposed adaptive intelligent
FPGA-back-stepping controller in terms of error reduction for the magnetic ball position and the smooth voltage
control action. These results were confirmed by a comparative study with different nonlinear controller
types.}
    }