• Smart Structures and Systems
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• v.2 no.2
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• pp.189-208
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• 2006

Monitoring large-scale civil engineering structures such as offshore platforms and high-large buildings requires a large number of sensors of different types. Innovative sensor data information technologies are very extremely important for data transmission, storage and retrieval of large volume sensor data generated from large sensor networks. How to obtain the optimal sensor set and placement is more and more concerned by researchers in vibration-based SHM. In this paper, a method of determining the sensor location which aims to extract the dynamic parameter effectively is presented. The method selects the number and place of sensor being installed on or in structure by through the tolerance domain statistical inference algorithm combined with second order sensitivity technology. The method proposal first finds and determines the sub-set sensors from the theoretic measure point derived from analytical model by the statistical tolerance domain procedure under the principle of modal effective independence. The second step is to judge whether the sorted out measured point set has sensitive to the dynamic change of structure by utilizing second order characteristic value sensitivity analysis. A 76-high-building benchmark mode and an offshore platform structure sensor optimal selection are demonstrated and result shows that the method is available and feasible.

• Proceedings of the KIEE Conference
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• summer
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• pp.345-347
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• 2004

In small signal stability analysis of power systems, eigenvalue analysis is the most useful method and the detailed modeling of generator has an important effect to the eigenvalues. Generator full model is used for precise dynamic analysis of generators and controllers while two-axis model is used for multi-machine systems because of the reduced order of the state matrix. Also, the eigenvalue sensitivity coefficients are used for optimizing controller parameters to improve system stability. This paper compare the first and second order eigenvalue sensitivity coefficients of controllers using generator full model with those of two-axis model. As a result of an example, the estimated eigenvalues using the first and the second eigenvalue sensitivity coefficients using generator full model is very close to those of state matrix. Also the error ratios throughout a wide range of controller parameters is less than $1\%$.

• Ocean Systems Engineering
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• v.1 no.4
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• pp.263-283
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• 2011

The performance of a rig tie-down system on a TLP (Tension Leg Platform) is investigated for 10-year, 100-year, and 1000-year hurricane environments. The inertia loading on the derrick is obtained from the three-hour time histories of the platform motions and accelerations, and the dynamic wind forces as well as the time-dependent heel-induced gravitational forces are also applied. Then, the connection loads between the derrick and its substructure as well as the substructure and deck are obtained to assess the safety of the tie-down system. Both linear and nonlinear inertia loads on the derrick are included. The resultant external forces are subsequently used to calculate the loads on the tie-down clamps at every time step with the assumption of rigid derrick. The exact dynamic equations including nonlinear terms are used with all the linear and second-order wave forces considering that some dynamic contributions, such as rotational inertia, centripetal forces, and the nonlinear excitations, have not been accounted for in the conventional engineering practices. From the numerical simulations, it is seen that the contributions of the second-order sum-frequency (or springing) accelerations can be appreciable in certain hurricane conditions. Finally, the maximum reaction loads on the clamps are obtained and used to check the possibility of slip, shear, and tensile failure of the tie-down system for any given environment.

• Ocean Systems Engineering
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• v.6 no.1
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• pp.1-21
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• 2016

The global performance of the 5 MW OC4 semisubmersible floating wind turbine in random waves with or without steady/dynamic winds is numerically simulated by using the turbine-floater-mooring fully coupled dynamic analysis program FAST-CHARM3D in time domain. The numerical simulations are based on the complete second-order diffraction/radiation potential formulations along with nonlinear viscous-drag force estimations at the body's instantaneous position. The sensitivity of hull motions and mooring dynamics with varying wave-kinematics extrapolation methods above MWL(mean-water level) and column drag coefficients is investigated. The effects of steady and dynamic winds are also illustrated. When dynamic wind is added to the irregular waves, it additionally introduces low-frequency wind loading and aerodynamic damping. The numerically simulated results for the 5 MW OC4 semisubmersible floating wind turbine by FAST-CHARM3D are also extensively compared with the DeepCWind model-test results by Technip/NREL/UMaine. Those numerical-simulation results have good correlation with experimental results for all the cases considered.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.77-87
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• 2021

In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.286-293
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• 2002

This paper presents a new method for finding the Block Pulse series coefficients and deriving the Block Pulse integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of continuous-time dynamic systems more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and drives the related integration operational matrices by using the Lagrange second order interpolation polynomial.

• Nuclear Engineering and Technology
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• v.43 no.3
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• pp.243-256
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• 2011

This paper considers the concept of analog computing based on a cellular neural network (CNN) paradigm to simulate nuclear reactor dynamics using a time-dependent second order form of the neutron transport equation. Instead of solving nuclear reactor dynamic equations numerically, which is time-consuming and suffers from such weaknesses as vulnerability to transient phenomena, accumulation of round-off errors and floating-point overflows, use is made of a new method based on a cellular neural network. The state-of-the-art shows the CNN as being an alternative solution to the conventional numerical computation method. Indeed CNN is an analog computing paradigm that performs ultra-fast calculations and provides accurate results. In this study use is made of the CNN model to simulate the space-time response of scalar flux distribution in steady state and transient conditions. The CNN model also is used to simulate step perturbation in the core. The accuracy and capability of the CNN model are examined in 2D Cartesian geometry for two fixed source problems, a mini-BWR assembly, and a TWIGL Seed/Blanket problem. We also use the CNN model concurrently for a typical small PWR assembly to simulate the effect of temperature feedback, poisons, and control rods on the scalar flux distribution.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.340-345
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• 1994

Dynamic hybrid position/force control of flexible manipulators is proposed. First, a 2 D.O.F. flexible manipulator is modeled using the spring-mass model. Second, the equation of motion considering the tip constraints is derived. Third, hybrid position/force control algorithm is derived. In this control algorithm, the differentiable order of the desired trajectory and the stability condition are different from the case of rigid manipulators. Lastly, to verify the effectiveness of the proposed control algorithm, simulation results are presented.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.311-318
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• 2002

Two adaptive nonlinear friction compensation schemes are proposed for second-order nonlinear mechanical systems with a partially known nonlinear dynamic friction model to achieve asymptotic position and velocity tracking. The first scheme has auxiliary filtered states so that a simple open-loop observer can be used. The second one has a dual-observer structure to estimate two different nonlinear aspects of the friction state. Conditions for the parameter estimates to converge to the true parameter values are presented. Simulation results are utilized to show control performance and to demonstrate the convergence of the parameter estimates to their true values.