Adaptive Predictive Control Based on Neural Networks

Dumitrache, I.; Constantin, Nicolae; Drăgoicea, Monica

doi:10.1016/s1474-6670(17)39362-x

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Future research will also include a part of the next directions already identified and proposed in (Hedrea et al, 2019) [17]: the derivation of other TPmodels for different plants, and the adaptation of results from other models and application areas. Such promising and also challenging plants and applications include robotics [28][29][30][31], fuzzy models and control [32][33][34][35][36][37][38], neural networks [39], medicine [40][41][42], servo systems and engines [43,44], supervisory control [45], and various modern optimization algorithms [46][47][48][49][50][51] applied to controller tuning and system model identification as well. low-speed maglev train, Journal of Vibroengineering, Vol.…”

Section: Discussionmentioning

confidence: 99%

Results on Tensor Product-based Model Transformation of Magnetic Levitation Systems

Hedrea¹,

Precup²,

Bojan-Dragoş³

2019

ACTA POLYTECH HUNG

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In this paper the TP-based model transformation method is used in order to obtain a Tensor Product-based model of magnetic levitation systems which approximates the behavior of the plant, but exhibiting a numerical approximation error. In order to test the derived TP model, the behavior of the TP model is compared to the laboratory equipment behavior taking into consideration five testing scenarios. Experimental results show that approximation errors are generally low, but depend on model parameters.

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