• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.1012-1015
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• 2011

The decision-directed blind equalization algorithm is often used due to its simplicity and good convergence property when the eye pattern is open. However, in a channel where the eye pattern is closed, the decision-directed algorithm is not guaranteed to converge. Hence, a modified Bussgang-type algorithm using a hyperbolic tangent function for zero-memory nonlinear(ZNL) function has been proposed and applied to avoid this problem by Filho et al. But application of this algorithm includes the calculation of hyperbolic tangent function and its derivative or a look-up table which may need a large amount of memory due to channel variations. To reduce the computational and/or hardware complexity of Filho's algorithm, in this paper, an improved method for the decision-directed algorithm is proposed. In the proposed scheme, the ZNL function and its derivative are respectively set to be the original signum function and a narrow rectangular pulse which is an approximation of Dirac delta function. It is shown that the proposed scheme, when it is combined with decision-directed algorithm, reduces the computational complexity drastically while it retains the convergence and steady-state performance of the Filho's algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2617-2622
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• 2003

The pH neutralization process has long been taken as a representative benchmark problem of nonlinear chemical process control due to its nonlinearity and time-varying nature. For general nonlinear processes, it is difficult to control with a linear model-based control method so nonlinear controls must be considered. Among the numerous approaches suggested, the most rigorous approach is the dynamic optimization. However, as the size of the problem grows, the dynamic programming approach is suffered from the curse of dimensionality. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach was proposed by Bertsekas and Tsitsiklis (1996). The NDP approach is to utilize all the data collected to generate an approximation of optimal cost-to-go function which was used to find the optimal input movement in real time control. The approximation could be any type of function such as polynomials, neural networks and etc. In this study, an algorithm using NDP approach was applied to a pH neutralization process to investigate the feasibility of the NDP algorithm and to deepen the understanding of the basic characteristics of this algorithm. As the global approximator, the neural network which requires training and k-nearest neighbor method which requires querying instead of training are investigated. The global approximator requires optimal control strategy. If the optimal control strategy is not available, suboptimal control strategy can be used even though the laborious Bellman iterations are necessary. For pH neutralization process it is rather easy to devise an optimal control strategy. Thus, we used an optimal control strategy and did not perform the Bellman iteration. Also, the effects of constraints on control moves are studied. From the simulations, the NDP method outperforms the conventional PID control.

• Computational Structural Engineering
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• v.10 no.3
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• pp.241-250
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• 1997

The strain localization of concrete is a phenomenon such that the deformation of concrete is localized in finite region along with softening behavior. The objective of this paper is to develop a plasticity and damage algorithm for the finite element analysis of the strain-localization in concrete. In this paper, concrete member under strain localization is modeled with localized zone and non-localized zone. For modeling of the localized zone in concrete under strain localization, a general Drucker-Prager failure criterion by which the nonlinear strain softening behavior of concrete after peak-stress can be considered is introduced in a thermodynamic formulation of the classical plasticity model. The return-mapping algorithm is used for the integration of the elasto-plastic rate equation and the consistent tangent modulus is also derived. For the modeling of non-localized zone in concrete under strain localization, a consistent nonlinear elastic-damage algorithm is developed by modifying the free energy in thermodynamics. Using finite element program implemented with the developed algorithm, strain localization behaviors for concrete specimens under compression are simulated.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.29 no.3
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• pp.253-260
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• 2016

In this paper, the optimal seismic design method for inducing the beam-hinge collapse mechanism of steel moment frames is presented. This uses the non-dominated sorting genetic algorithm II(NSGA-II) as an optimal algorithm. The constraint condition for preventing the occurrence of plastic hinges at columns is used to induce the beam-hinge collapse mechanism. This method uses two objective functions to minimize the structural weight and maximize the dissipated energy. The proposed method is verified by the application to nine story steel moment frame example. The minimum column-to-beam strength ratio to induce the beam-hinge collapse mechanism are investigated based on the simulation results. To identify the influence of panel zone on the minimum column-to-beam strength ratio, three analytic modeling methods(nonlinear centerline model without rigid end offsets, nonlinear centerline model with rigid end offsets, nonlinear model with panel zones) are used.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.801-804
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• 2003

The autopilot system of vessel is proposed to take service safety sorority, to elevate service efficiency, to decrease labor and to improve working environment. Ultimate purpose of it is to minimize the number of crew by guaranteeing economical efficiency of shipping service. Recently, the research is being achieving to compensate various nonlinear parameters of vessel and apply it is course keeping control, track keeping control, roll-rudder stabilization, dynamic ship positioning and automatic mooring control etc. using optimizing control technique. Relation between rudder angle controlled by steering machine of vessel and ship-heading angle, and load condition of ship are nonlinear, which affect various parameters of shipping service. The speed and direction of waves, velocity and quantity of wind, which also cause the non-linearity of it. Therefore the autopilot system of ship requires the robust control algorithm can overcome various non-linearity. On this paper, we design the autopilot system of ship, which overcome nonlinear parameters and disturbance of it using Fuzzy Algorithm, evaluate the proposed algorithm and its excellence through simulation

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1509-1513
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• 2003

The autopilot system of vessel is proposed to take service safety and security, to elevate service efficiency, to decrease labor and to improve working environment. Ultimate purpose of the proposed system is to minimize the number of crew by guaranteeing economical efficiency of shipping service. Recently, the research is being achieved to compensate various nonlinear parameters of vessel and apply it to course keeping control, track keeping control, roll-rudder stabilization, dynamic ship positioning and automatic mooring control etc. using optimizing control technique. Relation between rudder angle controlled by steering machine of vessel and ship-heading angle, and load condition of ship is nonlinear, which affects various parameters of shipping service. The speed and direction of waves, velocity and quantity of wind, which also cause the non-linearity of it. Therefore the autopilot system of ship requires the robust control algorithm can overcome various non-linearity. On this paper, we design the autopilot system of ship, which overcomes nonlinear Parameters and disturbance of it using Fuzzy Algorithm, evaluate the proposed algorithm and its excellence through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.57-62
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• 2004

This paper deals with a waypoint trajectory following problem for the tilt-rotor UAV under development in Korea (TR-KUAV). In this problem, dynamic model inversion based on the linearized model and Sigma-Phi neural network with adaptive weight update are involved to realize the waypoint following algorithm for the vehicle in the helicopter flight mode (nacelle angle=0 deg). This algorithms consists of two main parts: outer-loop system as a command generator and inner-loop system as stabilizing controller. In this waypoint following problem, the position information in the inertial axis is given to the outer-loop system. From this information, Attitude Command/Attitude Hold logic in the longitudinal channel and Rate Command/Attitude Hold logic in the lateral channel are realized in the inner-loop part of the overall structure of the waypoint following algorithm. The nonlinear simulation based on the TR-KUAV is carried out to evaluate the stability and performance of the algorithm. From the numerical simulation results, the algorithm shows very good tracking performance of passing the waypoints given. Especially, it is observed that ACAH/RCAH logic in the inner-loop has the satisfactory performance due to adaptive neural network in spite of the model error coming from the linear model based inversion.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1173-1177
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• 2015

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.56-61
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• 2017

The check valves are mechanical valves that permit fluids to flow in only one direction, preventing flow from reversing. It is classified as one way directional valves. There are various types of check valves that used in a marine application. A lift type check valve uses the disc to open and close the passage of fluid. The disc lift up from seat as pressure below the disc increases, while drop in pressure on the inlet side or a build up of pressure on the outlet side causes the valve to close. An important concept in check valves is the cracking pressure which is the minimum upstream pressure at which the valve will operate. On the other hand, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL(Nonlinear Programming by Quadratic Lagrangian) and genetic algorithm(GA) for optimization. NLPQL is the implementation of a SQP(sequential quadratic programming) algorithm. SQP is a standard method, based on the use of a gradient of objective functions and constraints to solve a non-linear optimization problem. A characteristic of the NLPQL is that it stops as soon as it finds a local minimum. Thus, the simulation results may be highly dependent on the starting point which user give to the algorithm. In this paper, we carried out optimization design of the check valve with NLPQL algorithm.

• Geomechanics and Engineering
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• v.10 no.1
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• pp.37-48
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• 2016

The hyperbolic stress-strain model has been shown to be valid for modeling nonlinear stress-strain behavior for rockfill materials. The Duncan-Chang nonlinear constitutive model was adopted to characterize the behavior of the modeled rockfill materials in this study. Accurately estimating the model parameters of rockfill materials is a key problem for simulating dam deformations during both the dam construction period and the dam operation period. In order to estimate model parameters, triaxial compression experiments of rockfill materials were performed. Based on a genetic algorithm, the constitutive model parameters of the rockfill material were determined from the triaxial compression experimental data. The investigation results show that the predicted strains provide satisfactory precision when compared with the observed strains and the strains forecasted by a gradient-based optimization algorithm. The effectiveness of the proposed inversion procedure of model parameters was verified by experimental investigation in a laboratory.