• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.135-141
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• 2008

In this paper a novel tracking system is described, regarding the influence of shadow between array, aimed at improving the efficiency of PV tracking system. Comparing with a building site versus capacity power, domestic solar powers have a limited siting. Therefore, each array interferes with the shadow of other arrays. The loss by influence of those shadow can be compensated for by means of control algorithm of the tracking device. The paper suggests a method controlling an altitude for length which is received the shadow influence of PV array. By using an azimuth of current solar position and the length between arrays, the controller of tracking device is able to calculate the length between actual arrays and make a comparison of the shadow length at a specific time with the length between arrays. When the shadow length is longer than the length between arrays, the controller of tracking device can adjust a position by compensating error altitude of the length between arrays at an altitude of current solar position. In the paper, we develop the control algorithm able to minimize the loss caused by the influence of shadow on the PV tracking system, and compared this with conventional output system. The controller has been tested in the laboratory with proposed algorithm and shows excellent performance

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.132-138
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• 2011

This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.502-507
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• 2016

In this paper, we propose a tracking control method for a quadrotor equipped with a 2-DOF manipulator, which is based on the multiple sliding surface control (MSSC) method. To derive the model of a quadrotor equipped with a 2-DOF manipulator, we obtain the models of a quadrotor and a 2-DOF manipulator based on the Lagrange-Euler formulation separately - and include the inertia and the reactive torque generated by a manipulator when these obtained models are combined. To make a quadrotor equipped with a manipulator track the desired path, we design a double-loop controller. The desired position is converted into the desired angular position in the outer controller and the system's angle tracks the desired angular position through the inner controller based on the MSSC method. We prove that the position-tracking error asymptotically converges to zero based on the Lyapunov stability theory. Finally, we demonstrate the effectiveness of the proposed control system through a computer simulation.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.176-178
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• 2003

This paper presents a new velocity estimation strategy of a non-salient permanent magnet synchronous motor(PMSM) drive without high frequency signal injection or special PWM pattern. This approach is based on the d-axis current regulator output voltage of the drive system which has the information of rotor position error. The rotor velocity can be estimated through a rotor position tracking PI controller that controls the position error to aero. For zero and low speed operation, the PI gains of rotor position tracking controller have a variable structure. The PI tuning formulas are derived by analyzing this control system using the frequency domain specifications such as phase margin and bandwidth assignment.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2962-2964
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• 1999

We know the traffic information about the velocity and position of vehicle by extraction and tracking vehicle from continuosly obtained road image of camera. The conventional method of vehicle detection indicate increment of error due to headlight and taillight in night road image. This paper show such as vehicle detection of binary, Edge detection. amalgamation of image are applied to extract the vehicle, and Kalman filter is adaptive methods for tracking position and velocity of vehicle.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.35-42
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• 2004

The control systems with friction cause the steady state error and slow response, because friction is a sensitive to the change of system condition and has highly nonlinear characteristics. To overcome these problems and do precise position control for a ball-screw system, we use Coulomb friction estimator and the sliding mode control(SMC) to compensate its negative effect. The applied SMC for tracking position has a characteristics of robust stability and reducing chattering, and is derived from the Lyapunov stability theorem and reaching condition. Compensating the estimated friction torque to the bounded disturbance term of the SMC's equivalent control input, it has a tracking performance better than the PID from the experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.515-526
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• 2003

Tutorial contents of kinematics and dynamics of a wheeled drive mobile robot are presented. Based on the dynamic model, simulation studies of position tracking of a mobile robot are performed. The control structure of several position control algorithms using visual feedback are proposed and their performances are compared. In order to compensate for uncertainties from unknown dynamics and ignored dynamic effects such as slip conditions, neural network based position control schemes are proposed. Experiments are conducted and the results show the performance of the vision based neural network control scheme fumed out to be the best among several proposed schemes.

• Journal of Power Electronics
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• v.13 no.6
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• pp.975-984
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• 2013

This paper proposes a robust optimal nonlinear control with an observer to reject the offset errors of position tracking for surface mounted permanent magnet synchronous motors. We provide the control method to reject offset errors and load torque for designing field oriented control (FOC) based the alternating current (AC) frame. The proposed method consists of a torque generator, a commutation scheme, an electrical controller, and a load torque observer. The mechanical controller is designed to compensate for load torque and the offset error and generate the desired torque. The commutation scheme is proposed to create the desired currents for the desired torque. The electrical controller is developed to guarantee the desired currents. The observer is designed to estimate both the velocity and the load torque. In order to obtain the robustness to parameter uncertainties and a gain tuning guide, the linear quadratic regulator method is applied to the proposed method. The closed-loop stability is proven. A detailed process for the FOC design and an analysis of the control methods based on the AC frame are presented. The performance of the proposed method was validated via experiments. The proposed method obtains the FOC based on the AC frame. Furthermore, the position tracking performance of the proposed method is superior to that of the conventional method.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.363-368
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• 2007

Most surgery illumination systems have been developed as passive systems. However, sometimes it is inconvenient to relocate the position of the illumination system whenever the surgeon changes his pose. To cope with such a problem, this study develops an auto-illumination system that is autonomously tracking the surgeon's movement. A 5-DOF serial type manipulator system that can control (X, Y, Z, Yaw, Pitch) position and secure enough workspace is developed. Using 3 ultrasonic sensors, the surgeon's position and orientation could be located. The measured data aresent to the main control system so that the robot can be auto-tracking the target. Finally, performance of the developed auto-illuminating system was verified through a preliminary experiment in the operating room environment.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.