• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.469-476
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• 2005

To date, many viable smart base isolation systems have been proposed. In this study, a novel friction pendulum system (FPS) and an MR damper are employed as the isolator and supplemental damping device, respectively. A fuzzy logic controller (FLC) is used to modulate the MR damper. A genetic algorithm (GA) is used for optimization of the FLC. The main purpose of employing a GA is to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. To this end, a GA with a local improvement mechanism is applied. Neuro-fuzzy models are used to represent dynamic behavior of the MR damper and FPS. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can find appropriate fuzzy rules and the GA-optimized FLC outperforms not only a passive control strategy but also a human-designed FLC and a conventional semi-active control algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1133-1136
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• 1993

Fuzzy algorithm is essentially nondeterministic, but to guarantee the stable control the fuzzy control program should be deterministic in practice. Fuzzy controllers with matrix representation is very simple in construction and very fast in computation. The value of the matrix is not adequate at the first place, but can be modified by using the neural networks. We apply the simple heuristic techniques to modify the matrix successfully.

• ICROS
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• v.1 no.1
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• pp.16-16
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• 1995

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.16-20
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• 1999

Like the usual systems, the industrial robot manipulator has some constraints for motion. Usually we hope that the manipulators move fast to accomplish the given task. The problem can be formulated as the time-optimal control problem under the constraints such as the limits of velocity, acceleration and jerk. But it is very difficult to obtain the exact solution of the time-optimal control problem. This paper solves this problem in two steps. In the first step, we find the minimum time trajectories by optimizing cubic polynomial joint trajectories under the physical constraints using the modified evolution strategy. In the second step, the controller is optimized for robot manipulator to track precisely the optimized trajectory found in the previous step. Experimental results for SCARA type manipulator show that the proposed method is very useful.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.598-603
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• 2004

An optical head actuator of an optical disk drive consists of two servo mechanisms for the focusing and the tracking to acquire data from disk. As the rotational speed of the disk grows, the utilized lag-lead-lead compensator has known to be above its ability for precisely controlling the optical head actuator. To overcome the difficulty, this paper propose a new controller design method for optical head actuator based fuzzy proportional-integral-derivative (PID) control and the genetic algorithm(GA). It employs a two-stage control structure with a fuzzy PI and a fuzzy PD control and is optimized by the GA to yield the suboptimal fuzzy PID control performance. It is shown the feasibility of the proposed method through a numerical tracking actuator simulation.

• Journal of Power Electronics
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• v.12 no.5
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• pp.787-799
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• 2012

Real and reactive power flows on a transmission line interact inherently. This situation degrades power flow controller performance when independent real and reactive power flow regulation is required. In this study, a quasi multi-pulse interline power flow controller (IPFC), consisting of eight six-pulse voltage source converters (VSC) switched at the fundamental frequency is proposed to control real and reactive power flows dynamically on a transmission line in response to a sequence of set-point changes formed by unit-step reference values. It is shown that the proposed hybrid fuzzy-PI commanded IPFC shows better decoupling performance than the parameter optimized PI controllers with analytically calculated feed-forward gains for decoupling. Comparative simulation studies are carried out on a 4-machine 4-bus test power system through a number of case studies. While only the fuzzy inference of the proposed control scheme has been modeled in MATLAB, the power system, converter power circuit, control and calculation blocks have been simulated in PSCAD/EMTDC by interfacing these two packages on-line.

• Ocean Systems Engineering
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• v.3 no.3
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• pp.237-255
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• 2013

In many applications, Remotely Operated Vehicles (ROVs) are required to be capable of course keeping, depth keeping, and height keeping. The ROV must be able to resist time-variant external forces and moments or frequent manipulate changes in some specified circumstances, which require the control system meets high precision, fast response, and good robustness. This study introduces a Fuzzy-Incomplete Derivative Ahead-PID (FIDA-PID) control system for a 500-meter ROV with four degrees of freedom (DOFs) to achieve course, depth, and height keeping. In the FIDA-PID control system, a Fuzzy Gain Scheduling Controller (FGSC) is designed on the basis of the incomplete derivative ahead PID control system to make the controller suitable for various situations. The parameters in the fuzzy scheme are optimized via many cycles of trial-and-error in a 10-meter-deep water tank. Significant improvements have been observed through simulation and experimental results within 4-DOFs.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.344-351
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• 2007

In this paper, we propose an approach to design multi-fuzzy controllers for the superheat and the low pressure that have an influence on energy efficiency and stabilization of air conditioning system with multi-evaporators. Air conditioning system with multi-evaporators is composed of compressor, condenser, several evaporators and several expansion valves. It is quite difficult to control the air conditioning system because the change of the refrigerant condition give an impact on the overall air conditioning system. In order to solve the drawback, we design multi-fuzzy controllers which control simultaneously both three expansion valve and one compressor for the superheat and the low pressure of air conditioning system. The proposed multi fuzzy controllers are given as a kinds of controller types such as a simplified fuzzy inference type. Here the scaling factors of each fuzzy controller are efficiently adjusted by Hierarchical Fair Competition-based Genetic Algorithms. The values of performance index of the simulation results of the A company type compare with simulation results of simplified inference type.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2176-2179
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• 2002

This paper introduces a hybrid induction motor control using a genetically optimized pseudo-on-line method. Optimization results from the use of a look-up table based on genetic algorithms to find the global optimum of a un-constraint optimization problem. The approach to induction motor control includes a pseudo-on-line procedure that optimally estimates parameters of a fuzzy PID (FPID) controller. The proposed hybrid genetic fuzzy PID (GFPID) controller is applied to speed control of a 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed controller is performs better than conventional FPID and PID controllers. The contribution of this paper is the introduction of a high performance hybrid form of induction motor control that makes on-line and real-time control of the drive system possible.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.05a
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.