• Journal of Power Electronics
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• v.17 no.6
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• pp.1587-1599
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• 2017

This paper proposes a real-time implementation of an optimal operation of a double stage grid connected wind power system incorporating an active power filter (APF). The system is used to supply the nonlinear loads with harmonics and reactive power compensation. On the generator side, a new adaptive neuro fuzzy inference system (ANFIS) based maximum power point tracking (MPPT) control is proposed to track the maximum wind power point regardless of wind speed fluctuations. Whereas on the grid side, a modified predictive current control (PCC) algorithm is used to control the APF, and allow to ensure both compensating harmonic currents and injecting the generated power into the grid. Also a type 2 fuzzy logic controller is used to control the DC-link capacitor in order to improve the dynamic response of the APF, and to ensure a well-smoothed DC-Link capacitor voltage. The gained benefits from these proposed control algorithms are the main contribution in this work. The proposed control scheme is implemented on a small-scale wind energy conversion system (WECS) controlled by a dSPACE 1104 card. Experimental results show that the proposed T2FLC maintains the DC-Link capacitor voltage within the limit for injecting the power into the grid. In addition, the PCC of the APF guarantees a flexible settlement of real power exchanges from the WECS to the grid with a high power factor operation.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.5
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• pp.548-555
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• 2007

The control of wastewater treatment has two merits; one is to regulate water quality of effluent and the other is to reduce the cost of wastewater treatment. The purpose of this study was to control the ammonium nitrogen in effluent that is known to cause eutrophication. The control was based on simplified ASM No. 1 which had 3 component materials and 8 coefficients, and the control method was as following. Firstly the ammonium concentration of inflow was measured and the optimal aeration time in effluent was determined according to simplified ASM No. 1 to be 1.0 mg/L. If ammonium concentration of effluent was not equal to 1.0 mg/L, the influent ammonium was corrected by adaptive control. These processes above were repeatedly performed. The SBR running aerobic-anoxic phase had been controlled for 1 month with this method. As a result, the ammonium concentration of the effluent showed in the range of $0.22\sim3.1$ mg/L with an average concentration of 1.1 mg/L. The adaptive control method used in this study was found very useful to control and predict the effluent concentration of ammonium.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1025-1031
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• 2011

Take-out robots used for handling of the plastic parts manufactured with the injection mold are usually the gantry type that consists of long and thin links, The performance of the take-out robot is determined by the speed of the motion and the positioning accuracy to grab the part out of the mold, As the speed of the robot increases the flexure in the links of the take-out robot becomes more significant and it results in more residual vibration, The residual vibration deteriorates the positioning accuracy and compels the operator to slow down the motion of the robot. The typical method to reduce the vibration in the robot requires stiffening the links and/or slowing down the robot, Vibration control could achieve the desired performance without increasing the manufacturing cost or the operation cost that would be incurred otherwise, Considering the point-to-point nature of the task to be performed by the take-out robot the time-delay command (or input) shaping filter approach would be the most effective control method to be adopted among a few available control schemes. In this paper a direct adaptive command shaping filter (ACSF) algorithm has been modified and applied to design the optimal command shaping filters for various configuration of the take-out robot. Optimal filters designed by ACSF algorithm have been implemented on a take-out robot and the effectiveness of the designed filters in terms of vibration suppression has been verified for multiple positions of the robot.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.858-863
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• 1988

A micro-processor based control system for a brushless DC motor used in the motor-driven electromechanical total artificial heart was developed. Functionally, the control system is composed of two parts. The first part is the velocity and position controller to assure that the motor follows a predetermined optimal velocity profile with minimal energy consumption, and to guarantee the full stroke length. This part also utilize the passive adaptive control method to be robust against the load disturbance, system parameter variation, and uncertainty which is the environment of artificial heart system. The pump output control is the second part, and this part provides the required responses of the artificial heart to the time-varying physiologic demands. The basic requirements of these responses are preload sensitivity, afterload insensitivity, and the balanced ventricular outputs. The performance and reliability of this control system was evaluated through a series of mock circulation tests and animal implantation, and the results are very encouraging.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.155-164
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• 2006

This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPI load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.702-704
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• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.47-54
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• 2019

In this paper, we consider a constant modulus (CM) based blind adaptive receiver design for downlink multi-carrier code-division multiple access (MC-CDMA) systems employing simple space-time block coding (STBC). In the paper, filter weight vectors used for the detection of the transmitted symbols are partitioned into its subvectors and then, special relations among the optimal subvectors minimizing the CM metric are derived. Using the special relations, we present a modified CM metric and propose a new blind adaptive stochastic-gradient CM algorithm (SG-CMA) by minimizing the modified CM metric. The proposed blind adaptive SG-CMA has faster convergence rate than the conventional SG-CMA because the filter weight vectors of the proposed scheme are updated in the region of satisfying the derived special relations. Computer simulation results are given to verify the superiority of the proposed SG-CMA.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.2
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• pp.53-63
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• 2018

A novel real-time path planning algorithm suitable for implementation on a small mobile robot is introduced. The algorithm can be used as the basis for mapping unknown or partially known environments and is tested in a specially developed simulation environment in Matlab(R). Simulations results are presented demonstrating that the algorithm can readily be implemented to allow a small robot to navigate in various unknown and partially known environments. The main characteristics of the algorithm include simplicity, ease of implementation, speed, and efficiency, thereby being especially suitable for small robots. Furthermore, for partially known environments, another algorithm is proposed to predefine an optimal path taking into account information provided regarding the environment.