Abstract
This paper presents a neural network (NN)-based non-linear dynamic modelling approach for a twin rotor multiple input, multiple output system (TRMS), in terms of its two-degrees-of-freedom dynamics. The TRMS is a highly non-linear system with significant cross-coupling between its horizontal and vertical axes. It is perceived as an aerodynamic test rig, representing the control challenges of modern air vehicles. Accurate dynamic modelling is a prerequisite to address such challenges satisfactorily. A feedforward NN has been trained using the Powell—Beale version of conjugate gradient and scaled conjugate gradient learning algorithms. The data for training comprises sine and square waves with various frequencies and amplitudes, pseudo random binary sequence (PRBS), and composite PRBS signals with different amplitudes. The trained NN-based models have been tested with a set of data that are different from those used for training purposes. For more validation, the power spectral density of the model is compared with that of the real TRMS and also the correlation validations of the test results are presented in order to show the effectiveness of the proposed model. The results show that the developed model can adequately represent the highly non-linear features of the system and can be used for sophisticated controller development.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.