Competency Test in Cricket Defensive Drive using Double Pendulum Dynamics

2019 ◽  
Author(s):  
Shadhon Chandra Mohonta ◽  
Ajay Krishno Sarkar
Keyword(s):  
Double Pendulum ◽  
Pendulum Dynamics

scholarly journals Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 186
Author(s):  
Fayeem Aziz ◽  
Aaron S.W. Wong ◽  
Stephan Chalup
Keyword(s):  
Manifold Learning ◽  
Network Architecture ◽  
Image Data ◽  
High Dimensional ◽  
Double Pendulum ◽  
Manifold Alignment ◽  
Series Of Experiments ◽  
Underlying Manifold ◽  
Low Dimensional ◽  
Pendulum Dynamics

The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data.

A single and double closed-loop compound anti-sway control method for double-pendulum bridge cranes locating at random position

2021 ◽  
pp. 014233122110464
Author(s):  
Minghui Xia ◽  
Xiaokai Wang ◽  
Qingxiang Wu ◽  
Lin Hua
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Smooth Transition ◽  
Double Pendulum ◽  
Bridge Crane ◽  
Special Equipment ◽  
Pendulum Model ◽  
Bumpless Transfer ◽  
Random Position ◽  
Pendulum Dynamics

In the assembly workshops of some heavy special equipment, the bridge cranes for payload lifting often needs to be located frequently. However, the locating position is often determined by the operator, which is random and results in significant payload oscillation and difficulties in trolley positioning. Furthermore, in practice, the bridge crane always exhibits more complicated double-pendulum dynamics compared with single-pendulum crane. To solve these problems, this paper establishes the double-pendulum model of bridge crane. Derived from the proportional-derivative (PD) control, the single closed-loop is designed based on the hook oscillation during acceleration and transporting; when locating, the double closed-loop is presented by utilizing the position and the hook oscillation. Combining the two control methods, a single and double closed-loop compound anti-sway control (SDCAC) method for the bridge crane is proposed. On this basis, to improve the performance of the SDCAC system, the sequential quadratic optimization (SQP) method is adopted to optimize PD parameters. Besides, a novel bumpless transfer control method is proposed to realize the smooth transition between the two control modes. Finally, the simulations and experiments are conducted. The results demonstrate the effectiveness of the proposed method.

Swing Dynamics and Input-Shaping Control of Human-Operated Double-Pendulum Boom Cranes

2012 ◽  
Vol 7 (3) ◽  
Author(s):  
Ehsan Maleki ◽  
William Singhose
Keyword(s):  
Material Handling ◽  
Vertical Axis ◽  
Input Shaping ◽  
Double Pendulum ◽  
Dynamic Effects ◽  
Construction Sites ◽  
Oscillatory Dynamics ◽  
Suspension Cable ◽  
Pendulum Dynamics ◽  
Input Shaping Control

Boom cranes are used for numerous material-handling and manufacturing processes in factories, shipyards, and construction sites. All cranes lift their payloads by hoisting them up using overhead suspension cables. Boom cranes move payloads by slewing their base about a vertical axis, luffing their boom in and out from the base, and changing the length of the suspension cable. These motions induce payload oscillation. The problem of payload oscillation becomes more challenging when the payload exhibits double-pendulum dynamics that produce two varying frequencies of oscillation. This paper studies the swing dynamics of such cranes. It also applies input shaping to reduce the two-mode oscillatory dynamics. Experiments confirm several of the interesting dynamic effects.

Input Shaping Control of Double-Pendulum Bridge Crane Oscillations

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
William Singhose ◽  
Dooroo Kim ◽  
Michael Kenison
Keyword(s):  
Single Mode ◽  
Input Shaping ◽  
Double Pendulum ◽  
Bridge Crane ◽  
Efficient Operation ◽  
Input Shaper ◽  
Large Amplitude Oscillation ◽  
Control Research ◽  
Pendulum Dynamics ◽  
Input Shaping Control

Large amplitude oscillation of crane payloads is detrimental to safe and efficient operation. Under certain conditions, the problem is compounded when the payload creates a double-pendulum effect. Most crane control research to date has focused on single-pendulum dynamics. Several researchers have shown that single-mode oscillations can be greatly reduced by properly shaping the inputs to the crane motors. This paper builds on those previous developments to create a method for suppressing double-pendulum payload oscillations. The input shaping controller is designed to have robustness to changes in the two operating frequencies. Experiments performed on a portable bridge crane are used to verify the effectiveness of this method and the robustness of the input shaper.

scholarly journals SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics

2020 ◽  
Vol 476 (2242) ◽  
pp. 20200279
Author(s):  
Kadierdan Kaheman ◽  
J. Nathan Kutz ◽  
Steven L. Brunton
Keyword(s):  
Nonlinear Dynamics ◽  
Measurement Data ◽  
Noisy Data ◽  
Rational Functions ◽  
Double Pendulum ◽  
Bz Reaction ◽  
Systems Models ◽  
Noise Robust ◽  
Pendulum Dynamics ◽  
Robust Variant

Accurately modelling the nonlinear dynamics of a system from measurement data is a challenging yet vital topic. The sparse identification of nonlinear dynamics (SINDy) algorithm is one approach to discover dynamical systems models from data. Although extensions have been developed to identify implicit dynamics, or dynamics described by rational functions, these extensions are extremely sensitive to noise. In this work, we develop SINDy-PI (parallel, implicit), a robust variant of the SINDy algorithm to identify implicit dynamics and rational nonlinearities. The SINDy-PI framework includes multiple optimization algorithms and a principled approach to model selection. We demonstrate the ability of this algorithm to learn implicit ordinary and partial differential equations and conservation laws from limited and noisy data. In particular, we show that the proposed approach is several orders of magnitude more noise robust than previous approaches, and may be used to identify a class of ODE and PDE dynamics that were previously unattainable with SINDy, including for the double pendulum dynamics and simplified model for the Belousov–Zhabotinsky (BZ) reaction.

Fulfillment of Arbitrary Movement Transfer Control between Equilibrium States for a Double Pendulum Robot

2011 ◽  
Vol 36 (12) ◽  
pp. 1720-1731 ◽  
Author(s):  
Zu-Shu LI ◽  
Yuan-Hong DAN ◽  
Xiao-Chuan ZHANG ◽  
Lin XIAO ◽  
Zhi TAN
Keyword(s):  
Equilibrium States ◽  
Double Pendulum
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