Polynomial regressors based data-driven control for autonomous underwater vehicles

Li, Hongran; Xu, Weiwei; Zhang, Heng; Zhang, Jian; Liu, Yi

doi:10.1007/s12083-020-00878-6

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…[28], extending the short-length data to a polynomial regression model [29][30][31]. Furthermore, polynomial-regression based on model-free-predictive control has also been applied to multi-input multi-output (MIMO) nonlinear systems [32], controlled systems attached by False Data Injection (FDI) [33], and autonomous underwater vehicles [34]. Although this method can be used in several applications, short-length data becomes longer when the order of polynomial increases, rendering some of the data in the systems unnecessary, while causing the performance of estimating future input to be volatile and out of control.…”

Section: Introductionmentioning

confidence: 99%

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Jiravit,

Yamamoto

2024

IEEJ Transactions Elec Engng

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One of the primary advantages of model‐free predictive control over conventional methods is that it does not use any mathematical models and relies only utilizes measured input/output data from the storage. The capability of model‐free predictive control has been already demonstrated in nonlinear systems using linear and polynomial regression for data storage. However, identifying the appropriate order that aligns with the actual system order remains a primary challenge, selecting an incorrect order may result in increasing redundant terms, ultimately leading to instability issues. In this study, we employed the Singular Value Decomposition (SVD) order selection technique, combined with the Bayesian Information Criterion (BIC), to identify the appropriate input and output orders of the system as well as the optimal horizon order in predictive control. This combined technique was subsequently applied to determine the appropriate order for model‐free predictive control. Our findings confirmed the effectiveness of the proposed method using numerical simulations in both linear and nonlinear systems. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Introductionmentioning

confidence: 99%

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Jiravit,

Yamamoto

2024

IEEJ Transactions Elec Engng

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“…To deal with model uncertainty and external disturbances, passive and active approaches are both designed for vessels. The active approach incorporates an adaptive observer-exploit estimation of the unknown item to indemnify these effects, and the passive approaches depend on the designed controller's robustness [10][11][12][13]. To ensure control performance and decrease the difficulty of controller design, the active approach is usually preferred to the passive approaches.…”

Section: Introductionmentioning

confidence: 99%

Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints

Zhang,

Xia

2023

Applied Sciences

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This manuscript investigates an output feedback-tracking control problem of a dynamically positioned vessel with an input constraint. The vessel is exposed to model uncertainty and external disturbances. Compared with the existing results, the primary contribution is to develop a switch-control strategy for achieving collision avoidance and performance constraints by using an extended state observer (ESO), a collision-avoidance controller (CAC), a prescribed performance controller, and an auxiliary dynamic system (ADS). The switch control strategy combined two different controllers, and an extended state observer (ESO) is designed. The ESO is employed to recover velocity information as well as unknown model uncertainty and external disturbances. A collision-risk-analysis module is introduced to evaluate whether there exists a risk of collision avoidance. Based on the analysis, the CASC can choose between a CAC and a PPC. An ADS is constructed to handle the input constraints. The CAC is employed by using an artificial potential function, the ADS, and the ESO. The PPC is designed based on an error constraint function, the ADS, and the ESO. The stability of the closed-loop control system is analyzed based on the Lyapunov direct method. Simulations prove the effectiveness of the presented control strategy.

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“…Some studies aim to reduce the impact of a system by optimizing the system structure. 4,5 Currently, the analysis of attack behaviors has become an academic focus for safeguarding system security. 6 Among the various attacks, the risk and complexity are higher due to FDI attacks that modify data directly at the network layer.…”

Section: Introductionmentioning

confidence: 99%

False data injection attacks detection based on Laguerre function in nonlinear Cyber‐Physical systems

Xia

et al. 2022

Internet Technology Letters

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This paper focus on the detection for false data injection (FDI) attacks. Under external conditions, the state and the output variables of Nonlinear Cyber‐Physical Systems (NCPSs) cannot be described by a linear relationship. It is difficult to detect the attacked systems. The Laguerre function model has excellent approximation capability to change structural parameters such as system delay and order, making it convenient for online parameter identification and suitable for complex industrial process control. Accordingly, in order to detect FDI attacks for NCPSs, the Laguerre function is adopted to detect FDI attacks and improve the detection rate. Numerical simulations verify the effectiveness of this method.

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Polynomial regressors based data-driven control for autonomous underwater vehicles

Cited by 8 publications

References 26 publications

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

A Two‐Stage Method for Order Selection in Model‐Free Predictive Control

Output Feedback Tracking Control for Vessel with Collision-Avoidance and Performance Constraints

False data injection attacks detection based on Laguerre function in nonlinear Cyber‐Physical systems

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