• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.421-423
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• 2007

In this paper, The authors apply a state feedback control using an optimal control theory to improve the stability of the control and the dynamic response of the DC-DC converter system with a number of different loads. To execute a this state feedback control, The authors present the pole placement technique using Linear Quadratic Regulator(LQR) to optimally control the system. An integrator can also be included in the open-loop path in order to minimize the steady-state error of the output voltage. To confirm the superiority of the controller, The simulation results are presented.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.5
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• pp.303-309
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• 2011

In this paper, an intelligent force control technique is applied to an autonomous helicopter. Although most research on the autonomous helicopter system is about navigation and control, force control of an autonomous helicopter system is quite new and not presented yet. After controlling the position of the helicopter by the LQR method, force control is applied. The adaptive impedance force control algorithm is introduced and tested to regulate the desired force under unknown location and stiffness of the environment. To compensate for uncertainty from outer disturbance, a neural network is added to form an intelligent force control framework. Simulation studies show that the proposed force control algorithm works well.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1195-1199
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• 1998

In this paper a design method of robust power system stabilizers is proposed by means of robust linear quadratic regulator design technique under power system's operating condition change, which is caused by inner structure uncertainties and disturbances into a power system. It is assumed that the uncertainties present in the system are modeled as one equivalent signal. In this connections an optimal LQR control input for disturbance rejection, the output feedback gain for eliminating the disturbance are calculated. In this case. PSS input signal is obtained on the basis of weighted ${\Delta}P_e$ and $\Delta\omega$. In order to stabilize the overall control of system. Pole placement algorithm is applied in addition. making the poles of the closed loop system to move into a stable region in the complex plane. Some simulations have been conducted to verify the feasibility of the proposed control method on a machine to infinite bus power system. From the simulation results validation of the proposed method could be achieved by comparisons with the conventional PSS with phase lag-lead compensation.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.110-115
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• 2004

The prosthesis of current commercialized apparatus has considerable problems, requiring improvement. Especially, LLP(Lower Limb Prosthesis)-related problems have improved, but it cannot provide normal walking because, mainly, the gait control of the LLP does not fit with patient's gait manner. To solve this problem, HCI((Human Computer Interaction) that adapts and controls LLP postures according to patient's gait manner more effectively is studied in this research. The proposed control technique has 2 steps： 1) the multilayer neural network forecasts angles of gait of LLP by using the angle of normal side of lower limbs； and 2) the adaptive neural controller manages the postures of the LLP based on the predicted joint angles. According to the experiment data, the prediction error of hip angles was 0.32［deg.］, and the predicted error of knee angles was 0.12［deg.] for the estimated posture angles for the LLP. The performance data was obtained by applying the reference inputs of the LLP controller while walking. Accordingly, the control performance of the hip prosthesis improved by 80% due to the control postures of the LLP using the reference input when comparing with LQR controller.