Dynamical Neural Models in Experimental Identification of Helicopter Educational Model—Methodology Proposal
Source abstract
Abstract
A design of methodology for experimental identification of nonlinear dynamical systems using feed-forward MLP neural networks as dynamical neural models is presented in this paper. The proposed methodology consists of four modules and makes use of already existing models of the nonlinear dynamical system. The methodology is verified in the case study, which is dedicated to the experimental identification of the modified helicopter educational plant CE 150 from Humusoft company. The previously created gray-box model of the helicopter educational model is used to estimate optimal neural network structure. The obtained black-box helicopter model, represented by a dynamical neural model, captures interactions between elevation and azimuth subsystems. The dynamical neural model is used to design three control algorithms without subsystem decoupling that are verified in the simulation environment and on the real helicopter educational model.
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Research context
OpenAlex topics: Control Systems and Identification · Aerospace and Aviation Technology · Advanced Control and Stabilization in Aerospace Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice, Slovakia
Funding
project ALICE experiment at the CERN LHC
References (25)
Show source-provided references
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