scholarly journals Dynamic modelling and open-loop control of a twin rotor multi-input multi-output system

Author(s):  
S M Ahmad ◽  
A J Chipperfield ◽  
M O Tokhi

A dynamic model for a one-degree-of-freedom (DOF) twin rotor multi-input multi-output (MIMO) system (TRMS) in hover is obtained using a black-box system identification technique. The behaviour of the TRMS in certain aspects resembles that of a helicopter; hence, it is an interesting identification and control problem. This paper investigates modelling and open-loop control of the longitudinal axis alone, while the lateral axis movement is physically constrained. It is argued that some aspects of the modelling approach presented are suitable for a class of new generation or innovative air vehicles with complex dynamics. The extracted model is employed for designing and implementing a feedforward/open-loop control. Open-loop control is often the preliminary step for development of more complex feedback control laws. Open-loop control strategies using shaped command inputs are accordingly investigated for resonance suppression in the TRMS. Digital low-pass and band-stop filter shaped inputs are used on the TRMS testbed, based on the identified vibrational modes. A comparative performance study is carried out and the corresponding results presented. The low-pass filter is shown to result in better vibration reduction.

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hong-tao Zhen ◽  
Xiao-hui Qi ◽  
Jie Li ◽  
Qing-min Tian

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of anL1adaptive controller and an auxiliary neural network (NN) compensation controller. TheL1adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.


2008 ◽  
Vol 50 (11) ◽  
pp. 2983-2986 ◽  
Author(s):  
Kumud Ranjan Jha ◽  
Neeraj Nehra

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI

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