• Smart Structures and Systems
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• v.14 no.6
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• pp.1081-1103
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• 2014

Real-time hybrid simulation (RTHS) has emerged as an important tool for testing large and complex structures with a focus on rate-dependent specimen behavior. Due to the real-time constraints, accurate dynamic control of servo-hydraulic actuators is required. These actuators are necessary to realize the desired displacements of the specimen, however they introduce unwanted dynamics into the RTHS loop. Model-based actuator control strategies are based on linearized models of the servo-hydraulic system, where the controller is taken as the model inverse to effectively cancel out the servo-hydraulic dynamics (i.e., model-based feedforward control). An accurate model of a servo-hydraulic system generally contains more poles than zeros, leading to an improper inverse (i.e., more zeros than poles). Rather than introduce additional poles to create a proper inverse controller, the higher order derivatives necessary for implementing the improper inverse can be calculated from available information. The backward-difference method is proposed as an alternative to discretize an improper continuous time model for use as a feedforward controller in RTHS. This method is flexible in that derivatives of any order can be explicitly calculated such that controllers can be developed for models of any order. Using model-based feedforward control with the backward-difference method, accurate actuator control and stable RTHS are demonstrated using a nine-story steel building model implemented with an MR damper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.245-248
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• 2006

A particle filter is one of the most famous filters. The reason why the particle filter is widely used is that particle deals with the state estimation problem for not only linear models with Gaussian noise but also the non-linear models with non-Gaussian noise and it receives great attention from many engineering fields. In the point of view state estimator, particle filter is feedforward observer. According to the characteristic of dynamic system, the feedforward observer can estimate real state. However, the speed of convergence of feedforward observer between the actual state and the estimated state cannot be satisfied. Since the particle filter is a sort of feedforward observer, the convergence speed of particle filter is slow, and the particle filter cannot estimate actual state like particle collapse problem. In order to overcome the limitation of particle filter as a kind of feedfoward estimator, we propose a new particle filter which has feedback term, called particle filter with feedback. Our proposed method is analyzed theoretically and studied by computer simulation. Comparisons are made with other filtering mehod.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.129-137
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• 2016

In the present study we verified performance of feed-forward control algorithm using short term prediction of ship motion information by taking advantage of developed numerical simulation model of FPSO motion. Up until now, various studies have been conducted about thrust control and allocation for dynamic positioning systems maintaining positions of ships or marine structures in diverse sea environmental conditions. In the existing studies, however, the dynamic positioning systems consist of only feedback control gains using a motion of vessel derived from environmental loads such as current, wind and wave. This study addresses dynamic positioning systems which have feedforward control gain derived from forecasted value of a motion of vessel occurred by current, wind and wave force. In this study, the future motion of vessel is forecasted via Brown's Exponential Smoothing after calculating the vessel motion via a selected mathematical model, and the control force for maintaining the position and heading angle of a vessel is decided by the feedback controller and the feedforward controller using PID theory and forecasted vessel motion respectively. For the allocation of thrusts, the Lagrange Multiplier Method is exploited. By constructing a simulation code for a dynamic positioning system of FPSO, the performance of feedforward control system which has feedback controller and feedforward controller was assessed. According to the result of this study, in case of using feedforward control system, it shows smaller maximum thrust power than using conventional feedback control system.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.288-293
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• 2009

This study presents the active noise control of duct noise. The duct was excited by a steady-state harmonic and white noise force and the control was performed by one control speaker attached to surface of the duct. An adaptive controller based on filtered x LMS(FXLMS) algorithm was used and controller was defined by minimizing the square of the response of the error microphone. The assemble controller, which is called a hybrid ANC(active noise control) system, was combined with feedforward and feedback controller. The feedforward ANC attenuates primary noise that is correlated with the reference signal, while the feedback ANC cancels the narrowband components of the primary noise that are not observed by the reference sensor. Furthermore, in many ANC applications, the periodic components of noise are the most intense and the feedback ANC system has the effect of reducing the spectral peaks of the primary noise, thus easing the burden of the feedforward ANC filter.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.221-227
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• 2024

In autonomous navigation systems, the need for fast and accurate image processing using deep learning and advanced sensor technologies is paramount. These systems rely heavily on the ability to process and interpret visual data swiftly and precisely to ensure safe and efficient navigation. Despite the critical importance of such capabilities, there has been a noticeable lack of research specifically focused on ship image classification for maritime applications. This gap highlights the necessity for more in-depth studies in this domain. In this paper, we aim to address this gap by presenting a comprehensive comparative study of ship image classification using two distinct neural network models: the Feedforward Neural Network (FNN) and the Convolutional Neural Network (CNN). Our study involves the application of both models to the task of classifying ship images, utilizing a dataset specifically prepared for this purpose. Through our analysis, we found that the Convolutional Neural Network demonstrates significantly more effective performance in accurately classifying ship images compared to the Feedforward Neural Network. The findings from this research are significant as they can contribute to the advancement of core source technologies for maritime autonomous navigation systems. By leveraging the superior image classification capabilities of convolutional neural networks, we can enhance the accuracy and reliability of these systems. This improvement is crucial for the development of more efficient and safer autonomous maritime operations, ultimately contributing to the broader field of autonomous transportation technology.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.2
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• pp.232-240
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• 1998

This paper deals with a hybrid position/force control of a robot which is moving on the constrained object with constant force. The proposed controller is composed of a position and force controller. The position controller has a nonlinear compensator which is based on the dynamic robot model and the force controller is attached by feedforward element. A direct drive robot with hard nonlinearity which is controlled by the proposed algorithm has moved on the constrained object with a high stiffness and low stiffness. The results show that the proposed controller has more vibration suppression effects which is occurred to the constrained object with a high stiffness, than a existing feedback controller, and accurate force control can be obtained by comparatively a small feedback gain.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.907-915
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• 2002

We present a state-space approach to design a passivity-based dynamic output feedback control of a finite collection of non-square linear systems. We first determine a squaring gain matrix and an additional dynamics that is connected to the systems in a feedforward way, then a static passivating (i.e. rendering passive) control law is designed. Consequently, the actual feedback controller will be the static control law combined with the feedforward dynamics. A necessary and sufficient condition for the existence of the parallel feedfornward compensator (PFC) is given by the static output feedback fomulation, which enables to utilize linear matrix inequality (LMI). The effectiveness of the proposed method is illustrated by some examples including the systems which can be stabilized by the proprotional-derivative (PD) control law.

• Journal of Mechanical Science and Technology
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• v.14 no.6
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• pp.645-654
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• 2000

In this paper, low level control algorithms of a metal belt CVT are suggested. A feedforward PID control algorithm is adopted for line pressure based on a steady state relationship between the input duty and the line pressure. Experimental results show that feedforward PID control of the line pressure guarantees a fast response while reducing the pressure undershoot which may result in belt slip. For ratio control, a fuzzy logic is suggested by considering the CVT shift dynamics and on-off characteristics of the ratio control valve. It is found from experimental results that a desired speed ratio can be achieved at steady state in spite of the fluctuating primary pressure. It is expected that the low level control algorithms for the line pressure and speed ratio suggested in this study can be implemented in a prototype CVT.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.369-373
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• 1992

Force control of a planar two-link structurally flexible robotic manipulator is considered in this study. The dynamic model is obtained by using the extended Hamilton's principle and the Galerkin criterion. A method is pressented toobtain the linearized equations of motion in Cartesian space for use in designing the control system. The approachto solving the control problem is to use feedforward and feedback control torques. The feedforward torques maneuver the flexible manipulatro along a nominal trajectory and the feedback torques minimize any deviations from the nominal trajectory. The linear quadratic Gaussian/loop transfer recovery (LQG/LTR) design methodology is explotied to design a robust feedback control system that can handle modeling errors and sensor noise, and operates on Cartesian space trajectory errors. The Lqg/LTR compenstaor together with a feedforward ollp is used to control the flexible manipulator. Simulated results are presented for a numerical example.