FPGA implementation of modified reconfigurable adaptive gain scheduling controller

Abstract EN

This article aims to put forward a modified type of adaptive gain scheduling that will be able to deal with the immeasurable and unpredictable variations of system variables by adapting its value at each time instance to follow any change in the input and overcome any disturbance applied to the system without the need to predetermine gains values.

In addition, the inverse neural controller will precede the gain scheduling to eliminate the need for complex system linear zing and parameter estimation.

Therefore, the problems of needing complex mathematics for system linearization and gains calculations have been solved.

The performance of the presented controller was tested by comparing the step response of a DC-motor controlled via the proposed technique and the response of that motor when controlled by the inverse neural controller and PID controller.

MATLAB/Simulink has been used for making the simulations and obtaining the results.

In addition, the FPGA implementation of the proposed controller has been presented.

The results showed a remarkable improvement in the transient response of the system for all of the rising time, delay time, settling time, peak overshoot, and steady-state This article aims to put forward a modified type of adaptive gain scheduling that will be able to deal with the immeasurable and unpredictable variations of system variables by adapting its value at each time instance to follow any change in the input and overcome any disturbance applied to the system without the need to predetermine gains values.

In addition, the inverse neural controller will precede the gain scheduling to eliminate the need for complex system linear zing and parameter estimation.

Therefore, the problems of needing complex mathematics for system linearization and gains calculations have been solved.

The performance of the presented controller was tested by comparing the step response of a DC-motor controlled via the proposed technique and the response of that motor when controlled by the inverse neural controller and PID controller.

MATLAB/Simulink has been used for making the simulations and obtaining the results.

In addition, the FPGA implementation of the proposed controller has been presented.

The results showed a remarkable improvement in the transient response of the system for all of the rising time, delay time, settling time, peak overshoot, and steady-state error.