Nonlinear Model Predictive Control of Dynamic Positioning of Deep-Sea Ships With a Unified Model

Wang, Yuanhui; Tuo, Yulong; Yang, Simon X.; Fu, Mingyu

doi:10.2316/journal.206.2016.6.206-4764

Cited by 5 publications

(5 citation statements)

References 19 publications

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“… I ( 3 n a + 3 n b + 3 ) × ( 3 n a + 3 n b + 3 ) is the ( 3 n a + 3 n b + 3 ) × ( 3 n a + 3 n b + 3 ) identity matrix. 23 In this paper, the adaptive GPC scheme for the DP system of ships performs the following steps at each sampling instant:…”

Section: Adaptive Gpc For Dp System Of Shipsmentioning

confidence: 99%

Adaptive generalized predictive control for dynamic positioning system of ships with model unknown parameters

Sun

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

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This paper proposes an adaptive generalized predictive control for dynamic positioning of ships with unknown model parameters and unknown time-varying environmental disturbances. The controlled auto-regressive integrated moving average (CARIMA) model is employed to describe the movements of ships with model parameter and disturbance uncertainties. The recursive least square method with forgetting factor is utilized to design adaptive laws to estimate the CARIMA model parameters online. Considering both the positioning accuracy and the change intensity of control increments, a cost function is constructed. The optimal control increments that minimize the cost function are obtained by using Lagrange multiplier method. Then, the adaptive control law is designed to maintain ship’s position and heading at desired values. Finally, simulation studies in different cases are carried out and simulation results demonstrate the effectiveness of the proposed control scheme.

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Section: Adaptive Gpc For Dp System Of Shipsmentioning

confidence: 99%

Adaptive generalized predictive control for dynamic positioning system of ships with model unknown parameters

Sun

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part M:

View full text Add to dashboard Cite

show abstract

“…It can be seen from (5) that α x and β x are constants when the time delay index Δk is determined, while in (6), α y and β y are time variants because the position interval Δx of x-axis in time delay Δk is changing. That is to say that the control signal of y-axis is relevant with position interval of x-axis.…”

Section: Position Domain Iterative Learning Controlmentioning

confidence: 99%

“…In some cases, each motion axis of a multi-input multi-output system is controlled by a separate control loop. A great deal of efforts concerning feedback and feedforward control algorithm, such as PID control [3], robust control [4], iterative learning control [5], predictive control [6] and master-slave control [7], were presented and implemented.…”

Section: Introductionmentioning

confidence: 99%

Precision Contour Tracking Using Feedback-Feedforward Integrated Control for a 2-Dof Manipulation System

Ling¹,

Feng²,

Min³

et al. 2018

International Journal of Robotics and Automation

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This paper presents a novel approach for precision contour tracking through combining feedback PID and feedforward position domain ILC (PDILC) control for a multi-axis manipulation system. Traditional control approaches in time domain suffer from poor synchronization of relevant motion axes and result in restriction for contour tracking tasks. In the proposed PID & PDILC design, a 2-DOF system is treated as a master-slave cooperative motion system. The position information of the master motion axis is integrated into the PDILC controller of the slave motion axes, which makes the PDILC learn from contour errors instead of individual axis errors. The selection and tuning of parameters for the PID and PDILC were conducted based on the computation in a lifted matrix format of the stability and convergence conditions. The performance of the PID & PDILC controller was evaluated by comparisons with PID and cross-coupled ILC controller through experiments on a multi-axis precise positioning stage. The proposed PID & PDILC design enhances the precision contour tracking of the testbed.

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“…According to our experiment, C ∈ [1 1.2]. Generally, overactive drivers tend to exhibit higher C. Here K is the same with that in (10). We need to renew the output of F 2 by…”

Section: Fatigue Level Classificationmentioning

confidence: 99%

“…Previous studies scarcely focused on SWA data in real driving for driver fatigue detection, which is inherently non-linear, time-variant and space-variant. Non-linear dynamic analysis [7]- [10], compared to classical linear time series analysis methods, can provide complementary information of the dynamic process. As a novel non-linear dynamic analysis method, approximate entropy algorithms have become popular and is proved to be robust for evaluating irregularity or predictability of time series [11].…”

Section: Introductionmentioning

confidence: 99%

Driver Fatigue Detection Using Approximate Entropic of Steering Wheel Angle From Real Driving Data

Li¹,

Li²,

Li³

et al. 2017

International Journal of Robotics and Automation

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This paper presents a steering-wheel-angle-based driver fatigue detection method for real driving conditions. This method extracts approximate entropy (ApEn) feature from recorded steering wheel angle (SWA) signal with a decision-tree-like classifier to identify the driving fatigue level. ApEn is extracted from fixed-size sliding window on real-time SWA series. To further exploit the in-depth information of SWA, additional features including intervalpercentage, deviation, kurtosis and complexity value of ApEn are extracted and applied to the designed classifier. The experiment is set on 14.68 h of real road driving, the collected data has been segmented into three fatigue levels ("awake , "drowsy , "very drowsy ).The classification result showed that the proposed method achieves an averaged accuracy of 82.07%. These results confirm that the proposed method is effective in the detection of real-time driver fatigue.

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Nonlinear Model Predictive Control of Dynamic Positioning of Deep-Sea Ships With a Unified Model

Cited by 5 publications

References 19 publications

Adaptive generalized predictive control for dynamic positioning system of ships with model unknown parameters

Adaptive generalized predictive control for dynamic positioning system of ships with model unknown parameters

Precision Contour Tracking Using Feedback-Feedforward Integrated Control for a 2-Dof Manipulation System

Driver Fatigue Detection Using Approximate Entropic of Steering Wheel Angle From Real Driving Data

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