Optimal quantitative H2 controller design for twin rotor MIMO system

Abstract EN

In this paper, the control approach is used for achieving the desired performance and stability of the twin-rotor MIMO system.

This system is considered one of the complex multiple inputs ofmultiple-output systems.

The complexity because of the high nonlinearity, significant cross-coupling and parameteruncertainty makesthe control of such systems is a very challenging task.

The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicoptersin many aspects.

The Quantitative Feedback Theory (QFT) controller is added to the control to enhance the control algorithm and to satisfy a more desirable performance.

QFT is one of the frequency domain techniques that is used to achieve a desirable robust control in presence of system parameters variation.

Therefore, a combination between control and QFT is presented in this paper to give a new efficient control algorithm.

On the other hand, to obtain the optimal values of the controller parameters, Particle Swarm Optimization (PSO) which is one of the powerful optimization methods is used.

The results show that the proposed quantitative control can achieve more desirable performance in comparison to control especially in attenuating the cross-coupling and eliminating the steady-state cross-coupling and parameteruncertainty makesthe control of such systems is a very challenging task.

The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicoptersin many aspects.

The Quantitative Feedback Theory (QFT) controller is added to the control to enhance the control algorithm and to satisfy a more desirable performance.

QFT is one of the frequency domain techniques that is used to achieve a desirable robust control in presence of system parameters variation.

Therefore, a combination between control and QFT is presented in this paper to give a new efficient control algorithm.

On the other hand, to obtain the optimal values of the controller parameters, Particle Swarm Optimization (PSO) which is one of the powerful optimization methods is used.

The results show that the proposed quantitative control can achieve more desirable performance in comparison to control especially in attenuating the cross-coupling and eliminating the steady-state error