• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.174-181
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• 1997

A proposed hybrid self-tuning control scheme for single rod hydraulic cylinder which has varying loads is presented here. An adaptive controller is developed for the system that use feedforward and P feedback control for simultaneous parameter identification and tracking control. Through experimental results, the performance comparison of the hybrid self-tuning controller with a constant gain P contro- ller clearly shows its superior ability in handling load changes in quiescent states.

• Journal of KSNVE
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• v.8 no.3
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• pp.441-449
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• 1998

A self-tuning control scheme, incorporated with the simplified 1st-order ARMAX(Auto-Regressive Moving Average eXogenous) model, for single rod hydraulic cylinder which has varying dynamic characteristics is presented here. An adaptive controller is developed for the system that uses feedforward and optimal feedback control for simultaneous parameter identification and tracking control. Through experimental results, the performance comparison of the self-tuning controller with a fixed gain proportional controller clearly shows its superior ability in handling load changes in quiescent states.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.236-238
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• 1992

This paper proposes a new algorithm to estimate time-varying parameters by combining KFSM(Kalman Filter with Shift Matrix) with neural network compansator. While the time varying parameters are estimated from KFSM, the error coverence of system, R(k) are compansated by neural network concurrently. The casestudy using computer simulation proves the usefullness and advantages of the proposed algorithm in this paper.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.192-197
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• 2007

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.8
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• pp.395-401
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• 2005

This paper presents Hybrid PI(HBPI) controller of induction motor drive using fuzzy control. In general, PI controllers used in computer numerically controlled machines process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of induction motor are presented to show the effectiveness of the proposed gam tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.301-308
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• 2018

This paper presents an optimal design method of a hybrid structural control system considering multi-hazard. Unlike a typical structural control system in which one system is designed for one specific type of hazard, a simultaneous optimal design method for both active and passive control systems is proposed for the mitigation of seismic and wind induced vibration responses of structures. As a numerical example, an optimal design problem is illustrated for a hybrid mass damper(HMD) and 30 viscous dampers which are installed on a 30 story building structure. In order to solve the optimization problem, a self-adaptive Harmony Search(HS) algorithm is adopted. Harmony Search algorithm is one of the meta-heuristic evolutionary methods for the global optimization, which mimics the human player's tuning process of musical instruments. A self-adaptive, dynamic parameter adjustment algorithm is also utilized for the purpose of broad search and fast convergence. The optimization results shows that the performance and effectiveness of the proposed system is superior with respect to a reference hybrid system in which the active and passive systems are independently optimized.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.52-56
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• 2004

Unmanned Aerial Vehicles (UAVs) are remotely piloted or self-piloted aircraft by inputted program in advance or artificial intelligence. In this study Aileron and Elevator are used to control the movement of airplane for horizontal and vertical flights about its longitudinal and lateral axis. In an introduction, the drone was linearly modeled by extracting aerodynamic parameter through flight test and simulation, lift and drag coefficient corresponding to angle of attack, changes of pitching moment coefficient. In the main subject, the flight simulation was performed after constructing hardware using TMS320F2812 from TI company and PID with lateral and longitudinal controller for horizontal and vertical flights. Flying characteristics of two system were estimated and compared through real flight test with hardware equipped algorithm and adaptive algorithm that was applied to consider external factors such as turbulence. In conclusion the control performance of the controller with proposed algorithm was streamlined at lateral and longitudinal controller respectively, we will discuss guidance command to pass way point.

• Magazine of the Korean Society of Agricultural Engineers
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• v.32 no.3
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• pp.47-55
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• 1990

An adaptive algorithm was applied to forecast daily stream flows in real time using rainfall data. A three-component tank model was selected to simulate the flows and its time-variant parameters were self-calibrated with updated data using a parameter optimization scheme, golden section search method. The resulting adaptive model, APTANK, was applied to six watersheds, ranging from 0.47 to 33.62 km$^2$ size and the simulated daily streamflows were compared with the measured. The simulation results were in good agreement with the field data. APTANK is found to be applied to real-time flow simulation purposes such as a tool for irrigation water resources management and operations. The model is particularly good to simulate streamflows on dry days as compared to wet days having runoff-induced precipitation.