2021
DOI: 10.1109/access.2020.3046517
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Predictive Control of a Supercavitating Vehicle Based on Time-Delay Characteristics

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Cited by 12 publications
(7 citation statements)
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“…In order to further verify the effectiveness of the H∞ method, the controller designed in this paper is applied to the planing force model ( 1). The dynamic model can be rewritten as The model is a LPV time-delay model of supercavitating vehicle, and the specific parameters in the model are shown in (Han et al, 2020). The H∞ state feedback controller is constructed, and the simulation verification is carried out.…”
Section: Simulation For Lpv Time-delay Model Of Supercavitating Vehiclementioning
confidence: 99%
See 1 more Smart Citation
“…In order to further verify the effectiveness of the H∞ method, the controller designed in this paper is applied to the planing force model ( 1). The dynamic model can be rewritten as The model is a LPV time-delay model of supercavitating vehicle, and the specific parameters in the model are shown in (Han et al, 2020). The H∞ state feedback controller is constructed, and the simulation verification is carried out.…”
Section: Simulation For Lpv Time-delay Model Of Supercavitating Vehiclementioning
confidence: 99%
“…Mao et al (Mao and Qian, 2009) used Vanek's delayed LPV model, but the coordinate point of Mao's model is at the head of the vehicle. Han et al proposed a predictive control for supercavitating vehicle based on time-delay characteristics, and the dynamic performance of the system was investigated (Han et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…W. Z. et al [9] used a maneuvering supercavity model with a cavitation number correction algorithm, proposing an exact linearizing feedback controller and a linear quadratic regulator for simulating the level flight, climbing, and diving of the vehicle. H. Y. et al [10] established a linear parameter-varying (LPV) time-delay model for a supercavitating vehicle by analyzing the time-delay characteristics of the planing force, and designed a predictive controller based on the model.…”
Section: Introductionmentioning
confidence: 99%
“…In the aspect of supercavitating vehicle modeling, the dynamic model contains 12 state variables and 6 degrees of freedom. Dzielski and Kurdila [1] proposed a dynamic benchmark model in the dive-plane which is widely adopted in the subsequent research [2][3][4][5][6], but the dynamic model in the reference [1] did not consider the cavity memory effect and the fin efficiency was set to a constant. Such simplifications more or less exist in the subsequent literatures.…”
Section: Introductionmentioning
confidence: 99%