• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

• International Journal of Ocean System Engineering
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• v.1 no.4
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• pp.192-197
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• 2011

This paper describes the mathematical modeling, control algorithm, system design, hardware implementation and experimental test of a Manta-type Unmanned Underwater Vehicle (MUUV). The vehicle has one thruster for longitudinal propulsion, one rudder for heading angle control and two elevators for depth control. It is equipped with a pressure sensor for measuring water depth and Doppler Velocity Log for measuring position and angle. The vehicle is controlled by an on-board PC, which runs with the Windows XP operating system. The dynamic model of 6DOF is derived including the hydrodynamic forces and moments acting on the vehicle, while the hydrodynamic coefficients related to the forces and moments are obtained from experiments or estimated numerically. We also utilized the values obtained from PMM (Planar Motion Mechanism) tests found in the previous publications for numerical simulations. Various controllers such as PID, Sliding mode, Fuzzy and $H{\infty}$ are designed for depth and heading angle control in order to compare the performance of each controller based on simulation. In addition, experimental tests are carried out in a towing tank for depth keeping and heading angle tracking.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.8-14
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• 2009

In this paper, it is proposed that the design of the PID controller with the internal model control structure for improved performance. Internal model was identification that is second-order plus dead time structure using final-value theorem and genetic algorithm The parameters of Controller are determined to minimize IAE(Integral of the Absolute value of the Error) and ITAE(Integral of the Time multiplied by the Absolute value of the Error) of performance index by internal model and numerical method. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.8 s.227
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• pp.1125-1134
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• 2004

This paper presents a mathematical model which is about the dynamics of not only a two wheel steering vehicle but a four wheel steering vehicle. A sliding mode ABS control strategy and PID rear wheel control logic are developed to improve the brake and cornering performances, and enhance the stability during emergency maneuvers. The performances of the controllers are evaluated under the various driving road conditions and driving situations. The numerical study shows that the proposed full car model is sufficient to accurately predict the vehicle response. The proposed ABS controller reduces the stopping distance and increases the vehicle stability. The results also prove that the ABS controller can be employed to a four wheel steering vehicle and improves its performance. The four wheel steering vehicle with PID rear wheel controller shows increase of stability when a vehicle speed is high and sharp cornering maneuver when a vehicle speed is low compared to that of a two wheel steer vehicle.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• Journal of the Society of Naval Architects of Korea
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• v.34 no.3
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• pp.19-26
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• 1997

This paper is concerned with the design of neural-network based autopilot control system. The back-propagation neural network introduced in the previous paper by authors is applied to the autopilot control system. As a result, two neural-network controllers are developed, which are the model reference adaptive neural controller and the instantaneous optimal neural controller. The model reference adaptive neural controller is the control technique that the heading angle and angular velocity are controlled by the rudder angle to follow the output of the reference model. The instantaneous optimal neural controller optimizes the transition from one state to the next state. These control techniques are applied to a simple ship maneuvering model and their effectiveness is proved by numerical examples.

• Journal of Korean Association for Spatial Structures
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• v.13 no.2
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• pp.37-45
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• 2013

Reduction of microvibration is regarded as important in high-technology facilities with high precision equipments. In this paper, smart control technology is used to improve the microvibration control performance. Mr damper is used to make a smart base isolation system amd fuzzy logic control algorithm is employed to appropriately control the MR damper. In order to develop optimal fuzzy control algorithm, a multi-objective genetic algorithm is used in this study. As an excitation, a train-induced ground acceleration is used for time history analysis and three-story example building structure is employed. Microvibration control performance of passive and smart base isolation systems have been investigated in this study. Numerical simulation results show that the multi-objective genetic algorithm can provide optimal fuzzy logic controllers for smart base isolation system and the smart control system can effectively reduce microvibration of a high-technology facility subjected to train-induced excitation.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.1-7
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• 2008

This paper presents a method to design a repetitive controller that is robust to variations in the system parameters. The uncertain parameters are specified probabilistically by their probability distribution functions. Instead of working with the distribution functions directly, the repetitive controller is designed from a set of models that are generated from the specified probability functions. With this multiple-model design approach, any number of uncertain parameters that follow any type of distribution functions can be treated. furthermore, the controller is derived by minimizing a frequency-domain based cost function that produces monotonic convergence of the tracking error as a function of repetition number. Numerical illustrations show how the proposed multiple-model design method produces a repetitive controller that is significantly more robust than an optimal repetitive controller designed from a single nominal model of the system.

• Smart Structures and Systems
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• v.23 no.5
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• pp.467-478
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• 2019

Composite floor structures formed by continuous slab panels may be susceptible to excessive vibrations, even when properly designed in terms of ultimate limit state criteria. This is due to the inherent vibration characteristics of continuous floor slabs composed by precast orthotropic reinforced concrete panels supported by steel beams. These floor structures display close spaced multimode vibration frequencies and this dynamic characteristic results in a non-trivial vibration problem. Structural stiffening and/or insertion of struts between floors are the usual tentative solution applied to existing vibrating floor structures. Such structural alterations are in general expensive and unsuitable. In this paper, this vibration problem is analyzed on the basis of results obtained from experimental measurements in typical composite floors and their theoretical counterpart obtained with computational modeling simulations. A passive control system composed by multiple synchronized dynamic attenuators (MSDA) was designed and installed in these floor structures and its efficiency was evaluated both experimentally and through numerical simulations. The results obtained from experimental tests of the continuous slab panels under human walking dynamic action proved the effectiveness of this control system in reducing vibrations amplitudes.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.93-102
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• 2006

Control input's saturation of active control devices for large structures under large external disturbances are often occurred. It is more difficult to obtain the exact values of mass and stiffness as structures are higher. The modelling errors between mathematical models and real structures must be also included as parameter uncertainties. Therefore, in active vibration control of civil engineering structures like buildings and bridges, the robust saturation controller design method considering both control input's saturation and parameter uncertainties of system is needed. In this paper, stabilities of linear optimal controller LQR, modified bang-bang controller, saturated sliding mode controller, and robust saturation controller among various controllers which have been studied and applied to active vibration control of buildings are investigated. Especially, unstable phenomena of the LQR, the modified bang-bang controller and the saturated sliding mode controller when the control input is saturated or parameter uncertainties exist are presented to show the necessity of the robust saturation controller. The robust stability of the robust saturation controller are shown through a numerical example of a 2DOF linear vibrating system and an experimental test of the two-story structure with an active mass damper (AMD).