• 한국산업융합학회 논문집
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• 제19권1호
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• pp.18-30
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• 2016

In this paper, we propose a new technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

• 전력전자학회논문지
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• 제22권4호
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• pp.336-344
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• 2017

This paper presents the three-port DC-DC converter modeling and controller design procedure, which is part of the solid-state transformer (SST) to interface medium voltage AC grid to bipolar DC distribution network. Due to the high primary side DC link voltage, the proposed converter employs the three-level neutral point clamped (NPC) topology at the primary side and 2-two level half bridge circuits for each DC distribution network. For the proposed converter particular structure, this paper conducts modeling the three winding transformer and the power transfer between each port. A decoupling method is adopted to simplify the power transfer model. The voltage controller design procedure is presented. In addition, the output current sharing controller is employed for current balancing between the parallel-connected secondary output ports. The proposed circuit and controller performance are verified by experimental results using a 30 kW prototype SST system.

• 제어로봇시스템학회논문지
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• 제6권9호
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• pp.813-821
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• 2000

In this paper, we developed modeling of tension and speed dynamics for a two-drum winder in a three span continuous web transport system which had not been previously. Dynamic modeling of the time-varying nonlinear system was derived by considering the effect of the radii and mass moment of inertia in the unwinder and the two-drum winder through winding up the web. After linearizing it, we designed with a variable-gain a PID controller for tension control and a PI controller for speed. Simulation is carried out with the variation of radii and moment of inertia at high speed for the proposed tension control system with the two-drum winder and the variavle-gain a PID controller. Results show good performance of tension control during the speed change speed at a start-up and stop.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.238-244
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• 2012

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot's position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 B
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• pp.1137-1139
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• 2002

In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gain is composed with the weights of RNN. For the on-line estimation of the weights of RNN, extended kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

• 제어로봇시스템학회논문지
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• 제6권2호
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1635-1645
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• 2015

This study proposes high-performance voltage controller design that employs a capacitor current control model for single-phase stand-alone inverters. The single-phase stand-alone inverter is analyzed via modeling, which is then used to design the controller. A design methodology is proposed to maximize the bandwidth of the feedback controller. Subsequently, to compensate for the problems caused by the bandwidth limitations of the controller, an error transfer function that includes the feedback controller is derived, and the stability of the repetitive control scheme is evaluated using the error transfer function. The digital repetitive controller is then implemented. The simulation and experimental results show that the performance of the proposed controller is high in a 1.5 kW single-phase stand-alone inverter prototype.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.538-541
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• 1995

Joint flexibilities and frictional uncertainties are known to be a major cause of performance degration in motion control systems. This paper investigates the modeling and compensation of these undesired effects. A hybrid controller, which consists of a predictive controller and a neural network controller, is designed to overcome these undesired effects. Also learning scheme for friction uncertainies, which don't interfere with feedback controller dynamics, is discussed. Through simulation works with two inetia-torsional spring system having Coulomb friction, the effectiveness of the proposed hybrid controller was tested. The proposed predictive & neural network hybrid controller shows better performance over one when only predictive controller used.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.513-518
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• 2001

As the machining depth increases, the drilling torque increases and fluctuates and the risk of drill failure also increases. Hence, drilling torque control is very important to prevent the drill from failure. In this study, a PID controller was designed to control the drilling torque in a machining center. The plant including the feed drive system, cutting process, and spindle system was modeled for controller design. The Ziegler-Nichols rule was used to determine the controller gain and control action times. The root locus plot was used to tune the controller gain for a certain cutting condition. Also, suggested was a simple method to obtain the tuned controller gain for an arbitrary cutting condition not using the Ziegler-Nichols rule and root locus plot. The cutting torque control, performance of the designed controller and the effect of gain tuning on the control performance were examined.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• 제3B권2호
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• pp.97-102
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• 2003

In this paper, a recurrent artificial neural network (RNN) based self-tuning speed controller is proposed for the high-performance drives of induction motors. The RNN provides a nonlinear modeling of a motor drive system and could provide the controller with information regarding the load variation system noise, and parameter variation of the induction motor through the on-line estimated weights of the corresponding RNN. Thus, the proposed self-tuning controller can change the gains of the controller according to system conditions. The gain is composed with the weights of the RNN. For the on-line estimation of the RNN weights, an extended Kalman filter (EKF) algorithm is used. A self-tuning controller is designed that is adequate for the speed control of the induction motor The availability of the proposed controller is verified through MATLAB simulations and is compared with the conventional PI controller.