• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.142-155
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• 2000

A MIMO model reference control scheme incorporating the variable structure theory for a vehicle four wheel steering system(4WS) is proposed and evaluated for a class of continuous-time nonlinear dynamics with known or unknown uncertainties. The scheme employs an neural network to identify the plant systems, where the neural network estimates the nonlinear dynamics of the plant. By the Lyapunov direct method, the algorithm is proven to be globally stable, with tracking errors converging to the neighborhood of zero. The merits of this scheme is that the global system stability is guaranteed and it is not necessary to know the exact structure of the system. With the resulting identification model which contains the neural networks, it does not need higher degrees of freedom vehicle model than 3 degree of freedom model. Th proposed scheme is applied to the active four wheel system and shows the validity is used to investigate vehicle handing performances. In simulation of the J-turn maneuver, the reduction of yaw rate overshoot of a typical mid-size car improved by 30% compared to a two wheel steering system(2WS) case, resulting that the proposed scheme gives faster yaw rate response and smaller side angle than the 2WS case.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.299-308
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• 2007

This study proposes a two-layer hierarchical control system that integrates active front wheel steering and four wheel braking torque control to improve vehicle handling performance and stability. The first layer is a robust model matching controller (R-MMC) based on linear matrix inequalities (LMIs), which optimizes an active front steering angle compensation and a desired yaw moment control, and calculates reference wheel slip for the target wheel according to the desired yaw moment. The second layer is a moving sliding mode controller (MSMC) that can track the reference wheel slip in a predetermined time by commanding proper braking torque on the target wheel to achieve the desired yaw moment. Since vehicle sideslip angle measurement is difficult to achieve in practice, a sliding mode observer (SMO) that requires only vehicle yaw rate as the measured input is also developed in this study. The performance and robustness of the SMO and the integrated control system are demonstrated through comprehensive computer simulations. Simulation results reveal the satisfactory tracking ability of the SMO, and the superior improved vehicle handling performance, stability and robustness of the integrated control vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.74.3-74
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• 2001

This paper proposes a nonlinear adaptive controller based on back-stepping method for tracking reference substrate concentration by manipulating dilution rate in a continuous baker´s yeast cultivating process in stirred tank bioreactor. Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The Haldane model for the specific growth rate depending on only substrate concentration is used in this paper. Due to the uncertainty of specific growth rate, it has been modified as a function including the unknown parameter with known bounded values. The substrate concentration in the bioreactor and feed line are measured. The deviation from the reference is observed when the external disturbance such as the change of the feed is introduced to the system ...

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.154-161
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• 2009

A fuel cell power plant is a very complex system which has various control loops with some non-linearity. For control of a fuel cell power plant, dynamic models of fuel cell stacks have been developed and simplified process flow diagrams of a fuel cell power plant has been presented. Using such a model of a Molten Carbonate Fuel Cell (MCFC) power plant, this paper deals with development of an intelligent setpoint reference governor (I-SRG) to find the optimal setpoints and feed forward control inputs for the plant power demand. The I-SRG is implemented with neural network by using Particle Swarm Optimization (PSO) algorithm based on system constraints and performance objectives. The feasibility of the I-SRG is shown through simulation of an MCFC power plant for tracking control of its power demand.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.103-109
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• 2008

The work to raise the bridge plate by using two hydraulic cylinders is very dangerous when generating the unbalance state between cylinders. For solving this problem, one cylinder is forced to follow the trajectory of another cylinder instead of applying the same trajectory to two cylinders at once. In this paper, the control method for dynamic stable on lifting the bridge plate is proposed. The simulation model is derived by using commercial software, AMESim and MatLab/simulink. The PID controller is designed on one cylinder for following the reference trajectory and the adaptive controller is designed on another cylinder for tracking the displacement of one cylinder. The performance improvement is shown by comparing the simulation results through computer simulation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.102-108
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• 2011

LQG/LTR control scheme is applied to the two axes stage using piezoelectric actuator for tracking reference input and suppressing hysteresis effect in this paper. The plant is combined with an integrator to improve the tracking ability. LQG/LTR controller is designed by making desirable target filter loop remove all poles except for an integrator included in new design plant model and loop transfer recovery. Decoupler in the shape of FIR filter is added to remove the coupling effect between the two axes motion and so feedback control loop is designed independently for the each axis motion.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.2
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• pp.27-32
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• 2015

A maximum power point tracking (MPPT) algorithm using fuzzy controller was considered. MPPT method was implemented based on the voltage and reference PV voltage value was obtained from Artificial Neural Network (ANN)-model of PV modules. Therefore, measuring only the PV module voltage is adequate for MPPT operation. Fuzzy controller is used to directly control dc-dc buck converter. The simulation results have been used to verify the effectiveness of the algorithm. The proposed method is compared with conventional PO(perturbation & observation), IC(Incremental Conductance) method. The nonlinearity and adaptiveness of fuzzy controller provided good performance under parameter variations such as solar irradiation.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.67-69
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• 2007

PMSM (Permanent Magnet Synchronous Motor) current control is a most inner loop of electromechanical driving systems and it plays a foundation role in the hierarchy's control loop of several mechanical machine systems. In this paper, a simple RMRAC control scheme for the PMSM is proposed in the synchronous frame. In the synchronous current model, the input signal is composed of as a calculated voltage by adaptive laws and system disturbances. The gains of feed-forward and feed-back controller are estimated by the proposed e-modification methods respectively, where the disturbances are assumed as filtered current tracking errors. After the estimation of the disturbances from the tracking errors, the corresponding voltage is fed forward to control input to compensate for the disturbances. The proposed method is robust to high frequency disturbances and has a fast dynamic response to time varying reference current trajectory. It also shows a good real-time performance duo to it's simplicity of control structure. Through the simulations considering several cases of external disturbances and experimental results, efficiency of the proposed method is verified

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.285-287
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• 2006

The paper treats the nonlinear robust control of Chua's circuit having Chuar's diode as an element based on the internal model principle. The Chua's diode has unknown nonlinear parameters and the circuits parameters are alos assumend unknown. Nonlinear regulator equations are established to obtain 3-fold equilibrium equations on which the output error is zero. Also an internal model of the 3-fold exosystem is constructed for obtaining the control law. Pole Placement method is used for obtaining the feeback control law. Simulation results are presented for tracking the sinusoidal and constant reference input signal. Asymptotic trajectory control and the suppression of chaotic motion in spite of uncertainties in the system are accomplished.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.126-133
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• 2000

This paper is a study on robust PID controller tuning technique using the frequency region model matching method.To design the robust PID controller satisfying disturbance attenuation and robust tracking property for a reference input first an {{{{ETA _$\infty$}}}} controller satisfying given performances is designed using an H$_{\infty}$ control method, And then the parameters(proportional gain integral gain and derivation gain) of the robust PID controller with the performances of the desinged H$_{\infty}$ controller are determined using the model matching method at frequency domain. in this paper this PID controller tuning technique is applied to PID speed controller design for BLDC motors. Consequently simulation results show that the proposed PID speed controller satisfies load torque disturbance attenuation and robust tracking property and this study has usefulness and applicability for the speed control system; design of BLDC motors.