Handling of Partially Filled Tank Containers by Means of Cranes

Volume 1B: Offshore Technology ◽

10.1115/omae2014-24160 ◽

2014 ◽

Author(s):

Andreas Hansen ◽

Edwin Kreuzer ◽

Christian Radisch

Keyword(s):

Fluid Dynamic ◽

Estimation Algorithm ◽

Sliding Modes ◽

Nonlinear Formulation ◽

Stabilizing Control ◽

Dimensional Approximation ◽

Low Dimensional ◽

And Control ◽

Liquid Dynamics ◽

Nonlinear State

Tank containers are widely used to transport a variety of liquid goods such as food products, oil, and different kinds of fuel including liquefied natural gas. Due to the unpredictable dynamic behavior of partially filled tank containers, regulations limit the containers to be either almost full (> 80%) or almost empty (< 20%), when handled by cranes. In order to provide arguments to ease these restrictions, the system is analyzed and control methods for assisting the crane operator are proposed. We deduce a very accurate and computationally favorable mathematical description of the coupled crane and fluid dynamics. The fluid is modeled by a potential flow approach resulting in a low dimensional approximation of the liquid dynamics. Coupling the fluid dynamic model with the load system model of a container crane leads to a nonlinear formulation of the overall system. The state estimation algorithm exclusively relies on the measured rope forces as well as the known motion parameters of the trolley and the rope winches. A nonlinear state feedback controller based on sliding modes for underactuated systems provides a stabilizing control signal for the system. Experimental results for validation of the model, the observer, and the control design are included.

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Low-dimensional approximation and control of periodic solutions in spatially extended systems

Physical Review E ◽

10.1103/physreve.58.361 ◽

1998 ◽

Vol 58 (1) ◽

pp. 361-368 ◽

Cited By ~ 23

Author(s):

S. Y. Shvartsman ◽

I. G. Kevrekidis

Keyword(s):

Periodic Solutions ◽

Extended Systems ◽

Dimensional Approximation ◽

Spatially Extended ◽

Spatially Extended Systems ◽

Low Dimensional ◽

And Control

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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽

10.3390/s21124068 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4068

Author(s):

Zheshuo Zhang ◽

Jie Zhang ◽

Jiawen Dai ◽

Bangji Zhang ◽

Hengmin Qi

Keyword(s):

Unscented Kalman Filter ◽

Time Estimation ◽

Estimation Algorithm ◽

Estimation Methods ◽

Fusion Algorithm ◽

State Variable ◽

Current Estimation ◽

Real Time Estimation ◽

And Control ◽

Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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A Low Dimensional Approximation For Competence In Bacillus Subtilis

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2015.2440275 ◽

2016 ◽

Vol 13 (2) ◽

pp. 272-280 ◽

Cited By ~ 1

Author(s):

An Nguyen ◽

Adam Prugel-Bennett ◽

Srinandan Dasmahapatra

Keyword(s):

Bacillus Subtilis ◽

Dimensional Approximation ◽

Low Dimensional

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Data fusion and simulation-based planning and control in cyber physical system for digital assembly of aeroplane

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962315500270 ◽

2015 ◽

Vol 06 (03) ◽

pp. 1550027 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Linxuan Zhang ◽

Tianyuan Xiao ◽

Jietao Dong

Keyword(s):

Data Fusion ◽

Hybrid Control ◽

Sliding Mode ◽

Estimation Algorithm ◽

Asynchronous Sampling ◽

Planning And Control ◽

Adaptive Sliding Mode Controller ◽

Simulation Based ◽

And Control ◽

Distributed Fusion

This paper introduces a CPS application for intelligent aeroplane assembly. At first, the CPS structure is presented, which acquires the characteristics of general CPS and enables "simulation-based planning and control" to achieve high level intelligent assembly. Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling, respectively. The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation, and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration, it can also directly link to data, and the error is smaller. This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control. A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness. At the same time, an outer collaborative trajectory planning is also designed. All the experiment results show the feasibility of above proposed methods.

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Heterogeneous Inertia Estimation for Power Systems with High Penetration of Converter-Interfaced Generation

Energies ◽

10.3390/en14165047 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5047

Author(s):

Diala Nouti ◽

Ferdinanda Ponci ◽

Antonello Monti

Keyword(s):

Power Systems ◽

Estimation Algorithm ◽

System Stability ◽

Rotational Inertia ◽

Heterogeneous Distribution ◽

Proper Actions ◽

High Penetration ◽

System Operator ◽

And Control ◽

System Inertia

The increasing and fast deployment of distributed generation is posing challenges to the operation and control of power systems due to the resulting reduction in the overall system rotational inertia and damping. Therefore, it becomes quite crucial for the transmission system operator to monitor the varying system inertia and damping in order to take proper actions to maintain the system stability. This paper presents an inertia estimation algorithm for low-inertia systems to estimate the inertia (both mechanical and virtual) and damping of systems with mixed generation resources and/or the resource itself. Moreover, the effect of high penetration of distributed energy resources and the resulting heterogeneous distribution of inertia on the overall system inertia estimation is investigated. A comprehensive set of case studies and scenarios of the IEEE 39-bus system provides results to demonstrate the performance of the proposed estimator.

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On-Line Chatter Detection Using Wavelet-Based Parameter Estimation

10.1115/imece2000-1801 ◽

2000 ◽

Author(s):

Taejun Choi ◽

Yung C. Shin

Keyword(s):

Parameter Estimation ◽

Estimation Algorithm ◽

Cutting Process ◽

High Dimensional ◽

Detection Accuracy ◽

Chatter Detection ◽

Ml Estimation ◽

On Line ◽

Low Dimensional ◽

Low Dimensional Dynamics

Abstract A new method for on-line chatter detection is presented. The proposed method characterizes the significant transition from high dimensional to low dimensional dynamics in the cutting process at the onset of chatter. Based on the likeness of the cutting process to the nearly-1/f process, this wavelet-based maximum likelihood (ML) estimation algorithm is applied for on-line chatter detection. The presented chatter detection index γ is independent of the cutting conditions and gives excellent detection accuracy and permissible computational efficiency, which makes it suitable for on-line implementation. The validity of the proposed method is demonstrated through the tests with extensive actual data obtained from turning and milling processes.

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Low Dimensional Models, Wavelet Transforms and Control

Eddy Structure Identification ◽

10.1007/978-3-7091-2676-9_5 ◽

1996 ◽

pp. 271-331 ◽

Cited By ~ 1

Author(s):

N. Aubry ◽

G. Berkooz ◽

B. Coller ◽

J. Elezgaray ◽

P. Holmes ◽

...

Keyword(s):

Wavelet Transforms ◽

Dimensional Models ◽

Low Dimensional ◽

And Control

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Low-Dimensional Approximation for Control of Spatio-Temporal Processes Using Principal Interaction Patterns

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.591-593.1217 ◽

2012 ◽

Vol 591-593 ◽

pp. 1217-1220

Author(s):

Xiang Ping Cao ◽

Zhao Yang Li ◽

Mei Xing Liu

Keyword(s):

Galerkin Approximation ◽

Basis Functions ◽

Interaction Patterns ◽

Infinite Dimensional ◽

Dynamical Behaviors ◽

Dimensional Approximation ◽

Spatio Temporal ◽

Low Dimensional Approximations ◽

Low Dimensional ◽

Advanced Model

Although the first-principal models of the spatio-temporal processes can accurately predict nonlinear and distributed dynamical behaviors, their infinite-dimensional nature does not allow their directly use. In this note, low-dimensional approximations for control of spatio-temporal processes using principal interaction patterns are constructed. Advanced model reduction approach based on spatial basis function expansion together with Galerkin method is used to obtain the low-dimensional approximation. Spatial structure called principal interaction patterns are extracted from the system according to a variational principle and used as basis functions in a Galerkin approximation. The simulations of the burgers equations has illustrated that low-dimensional approximation based on principal interaction patterns for spatio-temporal processes has smaller errors than more conventional approaches using Fourier modes or Empirical Eigenfunctions as basis functions.

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