Robust H∞ control algorithm for Twin Rotor MIMO System

Author(s):  
Lidiya John ◽  
Mija S.J

2013 ◽  
Vol 393 ◽  
pp. 688-693 ◽  
Author(s):  
Hanif Ramli ◽  
Wahyu Kuntjoro ◽  
M.S. Meon ◽  
K.M Asraf K. Ishak

This paper reports a current study on modeling and simulation of adaptive Active Force Control (AFC) based scheme embedded with an artificial neural network (ANN) and/or fuzzy logic (FL) in response manipulations of the twin rotor multi-input multi-output (MIMO) system (TRMS). TRMS is well known for its non-linear behaviour and common classical control scheme such as Proportional-Integral-Derivative (PID) would not be adequate to compensate disturbances. The disturbances in this case were as the results of non-linear external and internal parametric changes, namely angular momentum and couple reactions between the two axes of TRMS. The adaptive control algorithm was proposed in both pitch and yaw to generate an optimum control gain for both responses, simulated viz. MATLAB/SIMULINK software Package. The ANN and FL were integrated into the scheme and act as optimum control algorithm in catalyzing the performance of the TRMS. The results from hybrid conditions of PID-AFC, PID-AFC-ANN and PID-AFC-FL respectively were observed and analyzed. From performance evaluation, PID-AFC-FL scheme has demonstrated a potentially robust and effective manipulating capability in trajectory tracking.



2020 ◽  
Vol 38 (12A) ◽  
pp. 1880-1894
Author(s):  
Mustafa K. Khreabet ◽  
Hazem I. Ali

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 of multiple-output systems. The complexity because of the high nonlinearity, significant cross-coupling and parameter uncertainty makes the control of such systems is a very challenging task. The dynamic of the Twin Rotor MIMO System (TRMS) is the same as that in helicopters in 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.







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