• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.267-274
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• 2011

Recently, actor-critic methods have drawn significant interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. In this paper, we consider a new type of actor-critic algorithms employing the kernel methods, which have recently shown to be very effective tools in the various fields of machine learning, and have performed investigations on combining the actor-critic strategy together with kernel methods. More specifically, this paper studies actor-critic algorithms utilizing the kernel-based least-squares estimation and policy gradient, and in its critic's part, the study uses a sliding-window-based kernel least-squares method, which leads to a fast and efficient value-function-estimation in a nonparametric setting. The applicability of the considered algorithms is illustrated via a robot locomotion problem and a tunnel ventilation control problem.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2043-2050
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• 2017

This paper presents two MPC (Model Predictive Control) based charging and discharging algorithms of BESS (Battery Energy Storage System) for capacity firming of wind generation. To deal with the intermittency of the output of wind generation, a single BESS is employed. The proposed algorithms not only make the output of combined systems of wind generation and BESS track the predefined reference, but also keep the SoC (State of Charge) of BESS within its physical limitation. Since the proposed algorithms are both presented in simple if-then statements which are the optimal solutions of related optimization problems, they are both easy to implement in a real-time system. Finally, simulations of the two strategies are done using a realistic wind farm library and a BESS model. The results on both simulations show that the proposed algorithms effectively achieve capacity firming while fulfilling all physical constraints.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.615-620
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• 2001

This paper addresses the design of sliding model fuzzy-model-based controller using genetic algorithms. In general, the construction of fuzzy logic controllers has difficulties for the lack of systematic design procedure. To release this difficulties, the sliding model fuzzy-model-based controllers was presented by authors. In this proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Although, the stability and the performance is guaranteed by the proposed method, some design parameters have to be chosen by the designer manually. This problem can be solved by using genetic algorithms. The proposed method tunes the parameters of the controller, by which the reasonable accuracy and the control effort is achieved. The validity and the efficiency of the proposed method are verified through simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.1
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• pp.137-145
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• 1992

Considering the singularity of robot, singularity avoidance control of robot is very important. Because it is very difficult structurally to exclude the wrist singularity. Then new control policy is needed to overcome wrist singularity. In this paper, the singularity states of robot wrist was analyzed and control algorithms for 3 and 4 axes robot wrist were proposed. Application results of the proposed control algorithms to the path including singularity showed us their usefulness and validity.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.258-262
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• 1990

Several optimum control algorithms have been proposed to minimize the robot cycle time by velocity scheduling. Most of these algorithms assume that the dynamic and kinematic characteristics of a manipulator are fixed. This paper presents the study of a minimum-time optimum control for robotic manipulators considering parameter changes. A complete set of solutions for parameter identification of the robot dynamics has been developed. The minimum-time control algorithm has been revised to be updated using estimated parameters from measurements.

• International Journal of Advanced Culture Technology
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• v.4 no.2
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• pp.47-56
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• 2016

In transformer cooling systems, oil temperature is controlled through the use of a blower and oil pump. For this paper, set-point algorithms, a reset algorithm and control algorithms of the cooling system were developed by neural networks and fuzzy logics. The oil inlet temperature was set by a $2{\times}2{\times}1$ neural network, and the oil temperature difference was set by a $2{\times}3{\times}1$ neural network. Inputs used for these neural networks were the transformer operating ratio and the air inlet temperature. The inlet set temperature was reset by a fuzzy logic based on the transformer operating ratio and the oil outlet temperature. A blower was used to control the inlet oil temperature while the oil pump was used to control the oil temperature difference by fuzzy logics. In order to analysis the performance of these algorithms, the initial start-up test and the step change test were performed by using the dynamic model of a transformer cooling system. Test results showed that algorithms developed for this study were effective in controlling the oil temperature of a transformer cooling system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.6
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• pp.365-371
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• 2008

Condensing gas boiler units may make a big role for the reduction of energy consumption in heating industries. In order to decrease the energy consumption of a condensing gas boiler unit, effective operations of the system are necessary. In this study, mathematical models of a condensing gas boiler system were developed in order to develop control algorithms of the system. These include dynamic models of a blower, a gas valve, a pump, a burner, a boiler heat exchanger, and a hot water heat exchanger. Control algorithms of a blower, a gas valve, and a pump were also assumed. Simulation results showed good predictions of dynamic behaviors of a boiler system. Therefore, the simulation program developed for this study may be effectively used for the development of control algorithms of a boiler system.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.646-654
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• 2010

This paper describes an integrated chassis control for a maneuverability, a lateral stability and a rollover prevention of a vehicle by the using of the ESC and AFS. The integrated chassis control system consists of a supervisor, control algorithms and a coordinator. From the measured and estimation signals, the supervisor determines the vehicle driving situation about the lateral stability and rollover prevention. The control algorithms determine a desired yaw moment for lateral stability and a desired longitudinal force for the rollover prevention. In order to apply the control inputs, the coordinator determines a brake and active front steering inputs optimally based on the current status of the subject vehicle. To improve the reliability and to reduce the operating load of the proposed control algorithms, a multi-core ECU platform is used in this system. For the evaluation of this system, a closed loop simulations with driver-vehicle-controller system were conducted to investigate the performance of the proposed control strategy.