• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.83-91
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• 2004

The purpose of this paper is to obtain an accurate nonlinear system model to test various control schemes for a motion control system that requires high speed, robustness and accuracy. An industrial sewing machine equipped with a Brushless DC motor is considered. It is modeled by a neural network that is configured as an output-error dynamical system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a 2 degree-of-freedom PID controller to compensate the effects of disturbance without degrading tracking performance has been de-signed. In this experiment, it is not preferable for safety reasons to tune the controller online on the actual machinery. Experimental results confirm that the model is a good approximation of sewing machine dynamics and that the proposed control methodology is effective.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• Journal of Power Electronics
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• v.11 no.5
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• pp.719-725
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• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• The Transactions of the Korean Institute of Power Electronics
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• v.13 no.2
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• pp.119-127
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• 2008

In this paper, the modular line-connected photovoltaic PCS (photovoltaic power conditioning system) is proposed. A step-up DC-DC converter using a active-clamp circuit and a dual series-resonant rectifier is proposed to achieve a high efficiency and a high input-output voltage ratio efficiently. An IncCond (incremental conductance) MPPT (maximum power point tracking) algorithm that improves MPPT characteristic is used. The PV module current is estimated without using a DC current sensor. By control a inverter using a linearized output current controller, a unity power factor is achieved. All algorithms and controllers are implemented on a single-chip microcontroller and the superiority of the proposed DC-DC converter and controllers is proved by experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.704-712
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• 2016

Recently, the installation of photovoltaic (PV) systems has been increasing due to the worldwide interest in eco-friendly and infinitely abundant solar energy. However, the output power of PV systems is highly influenced by the surrounding environment. For instance, a string of PV systems composed of modules in series may become inoperable under cloudy conditions or when in the shade of a building. In other words, under these conditions, the existing control method of PV systems does not allow the string to be operated in the normal way, because its output voltage is lower than the operating range of the grid connected inverter. In order to overcome this problem, we propose a new control method using a DC-DC voltage regulator which can compensate for the voltage of each string in the PV system. Also, based on the PSIM S/W, we model the DC-DC voltage regulator with constant voltage control & MPPT (Maximum Power Point Tracking) control functions and 3-Phase grid connected inverter with PLL (Phase-Locked Loop) control function. From the simulation results, it is confirmed that the present control method can improve the operating efficiency of PV systems by compensating for the fluctuation of the voltage of the strings caused by the surrounding conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1500-1505
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• 2004

This paper presents the design and control of a small Brushless DC (BLDC) Motor for entertainment robots. In order to control the developed BLDC motor, Adaptive Fuzzy Control (AFC) scheme via Parallel distributed Compensation(PDC) is developed for the multi- input/multi-output plant model represented by the Takagi-Sugeno(TS) model. The alternative AFC scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to the velocity control of a developed small BLDC motor for entertainment robots.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.416-422
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• 2017

The robot control with safety consideration is required since robots and human work together in the same space more frequently in these days. For safety, robots must have compliance to human force and robust tracking performance with high impednace for the nonhuman disturbances. The novel idea is proposed to achieve the compliance and high impedance with one controller structure. For the compliance, the ISMC(Integral Sliding Mode Control) and HDOB(Human Disturbance Observer) The human force is identified by using the human band pass filter and its output is sent to the sliding surface. The sliding mode dynamic is affected by human disturbance and the compliance for human is achieved. The disturbances besides human frequencies are decoupled by the ISMC and the robust tracking is achieved. The additional LDOB(Low Frequency Disturbance Observer) decreases the maxim nonlinear gain and leads low chattering. The introduction of human disturbance into the sliding mode dynamic is the main novel idea of this paper.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.523-525
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• 2004

Over the last few years, the control of bipedal robot has been considered a promising research field in the community of robotics. But the problems we encounter make the control of a bipedal robot a hard task. The complicated link connection of the bipedal robot makes it impossible to achieve its exact model. In addition, the joint velocity is needed to accomplish good control performance. In this paper a control method using ANFIS as an system approximator is purposed. First a model biped robot of a biped robot with switching leg influence is presented. Unlike classical method, ANFIS approximation error estimator is inserted in the system for tuning the ANFIS. In the entire system, only ANFIS is used to approximate the uncertain system. ANFIS tuning rule is given combining the observation error, control error and ANFIS approximation error. But this needs velocity information which is not available. So a practical method is newly presented. Finally, computer simulation results is presented to show this control method has good position tracking performance and robustness without need for leg switching acknowledgement.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.95-105
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• 2001

In this paper, in order to control uncertain chaotic system, an adaptive fuzzy control(AFC) scheme is developed for the multi-input/multi-output plants represented by the Takagi-Sugeno(T-S) fuzzy models. The proposed AFC scheme provides robust tracking of a desired signal for the T-S fuzzy systems with uncertain parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the chaotic state tracks the state of the stable reference model(SRM) asymptotically with time for any bounded reference input signal. The suggested AFC design technique is applied for the control of an uncertain Lorenz system based on T-S fuzzy model such as stabilization, synchronization and chaotic model following control(CMFC).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.