• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.752-758
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• 2004

In this paper, we proposed an adaptivefuzzy sliding control algorithm using gradient descent method to reduce chattering phenomenon which is viewed in variable control system. In design of FLC, fuzzy control rules are obtained from expert's experience and intuition and it is very difficult to obtain them. We proposed an adaptive algorithm which is updated by consequence part parameter of control rules in order to reduce chattering phenomenon and simultaneously to satistfy the sliding mode condition. The proposed algorithm has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbance, parameter variations and uncertainties in the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to an inverted pendulum system. The results show that both alleviation of chattering and performance are achieved.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.2
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• pp.9-15
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• 2001

This paper describes a new method of Genetic Algorithms for Adaptive Fuzzy Control System. Previous works using a Multi-population Genetic Algorithm have divided chromosome into two components, rule sets and membership functions. However, in this case bad rule sets disturb optimization in good rule sets and membership functions. A new method for a Multi population Genetic Algorithm suggests three components, good rule sets, bad rule sets, and membership functions. To show the effectiveness of this method, fuzzy controller is applied to a Truck Backing Problem. Results of the computer simulation show good adaptation of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.2
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• pp.192-199
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• 2000

This paper describes a Phase-Shift Pulse Modulation(PSPM) and Frequency Trad이ng Pulse Modulation(FTPM) s series resonant high-frequency inverter using IGBT for the power control of high-frequency induction heating u using Neuro-Fuzzy, which is practically applied for 20kHz~500kHz induction-heating and melting power supply in i indust껴aJ fields. The adaptive frequency tracking based on the PSPM(phase-shifting pulse modulation) r regulation scherne is presented in or$\tau$ler to l11lmmlZe svvitching losses. The trially-produced breadboards using N Neuro Fuzzy controller are successfully demonstrated cUld cliscussed.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.85-89
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• 1998

A switching power stage is a very nonlinear system because it has two or more operation modes in one switching cycle. To model a switching power stage, the state space averaging method has been developed. Though it allows a unified treatment of a large variety of switching power stages, the model it yields is always very nonlinear. So, it is required to linearize the averaged model. But it is well known that a controller for a nonlinear plant designed by the linearization frequently fails in showing satisfactory control performance. Hence it is very natural to try to design a nonlinear controller for a switching power stage. In design of a switching power system, nonlinear control approaches such as adaptive control and fuzzy control have been widely studied so far. In this research, a recently developed control method, time delay control is briefly studied and a design example for a ZVS PWM half bridge converter is given. The performance of the time delay controller is compared to its conventional counterpart, PI controller by computer simulations.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.89-99
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• 2008

In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1476-1479
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• 1996

The ring rolling process is one of the best known ring production method. The present model based control system was designed for rings with rectangle cross-section yet. An Adaptive Fuzzy Control for Closed-Die Ring-Rolling was developed in order to enhance the flexibility of the radial-axial ring rolling machine and to produce the rings with highly complex cross-section profile, roller bearing rings. A fuzzy method was implemented because of its simple application and to utilize the known process knowledge. The quality of the control system was estimated by die filling grad, which is strong dependent on the rising time of the controller. The rolling process parameters were also varied to determine their influence on filling of the ring profile. Die filling met the requirement of the industry.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.776_777
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using neural network(NN). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. The design of the speed controller based on adaptive learning mechanism fuzzy-neural networks(ALM-FNN) controller that is implemented using fuzzy control and neural networks. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.