• 전자공학회논문지B
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• 제33B권2호
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• pp.140-148
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• 1996

This paper proposes a combined feedforward/feedback controller which uses jacobians of neural network. The jacobians are calculated form the neural network that identifies the nonlinear plant, which are used for designing a jacobian controller and for training a neural network controller. Normally, it takes much time to train the neural network controller. Combining the neural and the jacobian controller, it can be a stable controller from the beginning of training phase of neural network, and it can be implemented as a learning-while-functioning controller. Simulated resutls for the proposed controller show its effectiveness and better performances.

• Journal of Power Electronics
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• 제10권1호
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• pp.21-26
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• 2010

The most important feature of the one-cycle control method is its excellent ability in line disturbance rejection. However, when it is used as a controller in dc-dc converters, it has an undesirable transient response. The voltage overshoot at the transient time, which usually exists in one-cycle controlled converters, is unwanted in many applications and it is sometimes hazardous. In this paper, it is shown that the combination of a one-cycle controller with a current mode controller, can improve the transient response and consequently the overshoot can be controlled. Therefore, the combined controller has the excellent line disturbance rejection of a one-cycle controller and the output current limiting capability of current mode controllers. Because in this scheme a one-cycle controller is the master controller, the problem of instability of current mode control, will not happen. By simulation and a practical prototype, the capability of the method is shown.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 전기설비전문위원
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• pp.74-76
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• 2009

In this paper, search method and sliding mode observer are developed for efficiency optimization of induction motor. The proposed control scheme consists of efficiency controller and adaptive backstepping controller. A search controller for which information of input of fuzzy controller is included in efficiency controller that uses a direct vector controlled induction motor. The search controller is based on the "Rosenbrock" method and finds the flux level at the minimum input power of induction motor. Once this optimal flux level has been determined, this information is utilized to update the rule base of a fuzzy controller A sliding mode observer is designed to estimate rotor flux and an adaptive backstepping controller is also used to compensate for mechanical uncertainties in the speed control of induction motor. Simulation results are presented to validate the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.172.2-172
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• 2001

We introduce a new stable feedback controller eventually converges to a conventional SDRE(State Dependent Riccati Equation) based optimal (suboptimal) controller. On conventional SDRE, the optimal control input should be obtained by backward integration of the SDRE at each control point. The proposed controller is given by direct forward integration of a proposed SDRE. This fact enables fast computation and easy implementation. On concerning a state dependent system, the proposed controller may be a candidate to the conventional SDRE based optimal controller if the system is slow varying with states. Though the controller is fast and easy to implement it is not able to cope with a fast varying system. We introduce an optimality index, which indicates how far the proposed controller is deviated from the solution of the convectional SDRE. If the index escapes a ...

• 한국정밀공학회지
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• 제15권4호
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.412-415
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• 1999

In this paper, we describe an implementation of the B-Link bus interface logic for a directory controller and a remote access cash controller in the SCI-based CC-NUMA multimedia server developed by ETRI . The CC-NUMA multimedia server is composed of a number of Pentium III SHV nodes and a SCI interconnection network. To communicate with remote nodes, each node has a CC-Agent which consists of a processor bus interface(PIF). a directory controller(DC), a remote access cash controller(RC), and two SCI 1ink controllers(LCs). The B-Link bus interface logic is developed for a directory controller and a remote access cash controller in order to communicate with a SCI link controller on a B-Link bus. It consists of a sending master controller a receiving slave controller, and asynchronous data buffers. And It performs a self-arbitration, a data packet transmission, a queue allocation, an early terminal ion. and a cut-through data path.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.207-209
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• 1995

The PID controller is widely used due to its fast response and robustness. But its performance is not so good compared with modem controllers such as adaptive, robust, fuzzy, neural controller. Therefore, it is natural to replace PID controller by modem controllers. But, the problem is that modem controller can not be easily applied to the real time process. Hence, this paper proposes such a structure that PID controller and Self-Learning Fuzzy Controller(SLFC) are in parallel with each other. The parameter of SLFC will be updated by gradient descent method using neuro - identifier. The usefulness of this hybrid controller will be proved by simulation results.

• Journal of Power Electronics
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• 제7권1호
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• pp.21-27
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• 2007

This paper investigates a PID fuzzy controller for output voltage regulation of a current-doubler-rectified asymmetric half-bridge (CDRAHB) DC/DC converter. The controller is a PD-type fuzzy controller in parallel with a linear integral controller. The PD type fuzzy controller is for providing the varying gain at the different operating conditions to regulate the output voltage. The linear integral controller is for removing the steady-state error of the output voltage. In order to show the outstanding dynamic characteristics of the proposed controller, PSIM simulation studies are carried out and compared to the results for which the conventional loop gain design method is used.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.228-228
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• 2000

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require an accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller Parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is obtained through a series of experiments for the various types of input while applying disturbances to the cylinder. .and performance of this controller was compared with that of PID, PD controller. As a experimental result, it can be proven that the position tracking performance of the neuro-fuzzy is better than that of PID and PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.