• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.560-563
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• 2000

In this paper we proposed AC/DC boost converter to improve input current harmonic reduction in TIG welder. The proposed harmonic reduction circuit with UC2854AN acting on constant switching frequency average current control has a three-loop control structure : the inner current loop the line voltage feed-forward loop and th outer voltage loop. Also we applied the constant current strategy on full bridge IGBT inverter to stabilized the output current using the analog PI controller. To demonstrate the practical significance of the proposed methods some simulation studies and experimental results are presented.

• Journal of Electrical Engineering and information Science
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• v.3 no.1
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• pp.36-44
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• 1998

In this paper, a robust adaptive sliding mode control algorithm for accurate trajectory tracking of robot manipulators is proposed, with unknown parameters being estimated on-line. The controller is designed based on a Lyapunov method, which consists of adaptive feed-forward compensation part and a discontinuous control part. It is shown that, in the presence of the uncertainty and the disturbances arising from the actuator or some other causes, the tracking errors is bound to converge to zero asymptotically. An illustrative example is given to demonstrate the results of the propose method.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2227-2236
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• 2017

In order to solve the neutral-point voltage fluctuation problem of three-phase three-level T-type inverters (TPTLTIs), the unbalance characteristics of capacitor voltages under different switching states and the mechanism of neutral-point voltage fluctuation are revealed. Based on the mathematical model of a TPTLTI, a feed-forward voltage balancing control strategy of DC-link capacitor voltages error is proposed. The strategy generates a DC bias voltage using a capacitor voltage loop with a proportional integral (PI) controller. The proposed strategy can suppress the neutral-point voltage fluctuation effectively and improve the quality of output currents. The correctness of the theoretical analysis is verified through simulations. An experimental prototype of a TPTLTI based on Digital Signal Processor (DSP) is built. The feasibility and effectiveness of the proposed strategy is verified through experiment. The results from simulations and experiment match very well.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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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.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.516-518
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• 1999

This paper presents an feed-forward neural network design instead PI controller for the speed control of an Induction Motor. The design employs the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE). Emulator identifies the motor by simulating the input and output map. In order to update the weights of the Controller. Emulator supplies the error path to the output stage of the controller using backpropagation algorithm. and then Controller produces an adequate output to the system due to neural networks learning capability. Therefore it becomes adjustable to the system with changing characteristics caused by a load. The speed control based on neural networks for induction motor is implemented by a vector controlled induction motor. The simulation results demonstrate that actual motor speed with neural network system well follows the reference speed minimizing the error and is available to implement on the vector control theory.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.139-149
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• 2015

In this paper, we describe a new approach to perform real-time implementation of an robust controller for robotic manipulator based on digital signal processors in this paper. The Texas Instruments DSPs 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 direct Lyapunov 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 robot manipulator consisting of dual arm with eight degrees of freedom at the joint space and cartesian space.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.605-614
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• 1999

This paper addresses analysis and design of a class of complex single-input single-output fuzzy control systems. In the proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Therefore, the globally stable fuzzy controller is designed without finding a common Lyapunov matrix. and shows improved perfonnance and tracking results by taking the advantages of fuzzy-model-based control theory and sliding mode control theory. Furthennore, stability analysis is conducted not Ibr the fuzzy model but for the real underlying nonlinear system. Two numerical examples are included to show the effcctiveness and feasibility of the proposed fuzzy control method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1220-1230
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• 2006

In this paper, we propose a flew 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.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2286-2288
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• 2001

In this paper, a drive system of induction motor with high performance is realized on the viewpoint of the design and experiment, using the DSP (TMS320F240). The speed controller for induction motor drive system is designed on the basis of a neuro-fuzzy network. The neuro-fuzzy controller acts as a feed-forward controller that provides the right control input for the plant and accomplishes error back-propagation algorithm through the network. The proposed network is used to achieve the high speedy calculation of the space vector PWM (Pulse Width Modulation) and to build the neuro-fuzzy control algorithm, for the real-time control. The proposed neuro-fuzzy algorithm on the basis of DSP shows that experimental results have good performance for the precise speed control of an induction motor drive system. It is confirmed that the proposed controller could provide more improved control performance than conventional v/f vector controllers through the experiment.

• Journal of Biosystems Engineering
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• v.17 no.4
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• pp.396-403
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• 1992

In order to operate a combine harvester at the optimum conditions and maximum performance, a forward speed control system(FSCS) was designed and develped. The FSCS consisted of engine, continuously variable V-belt transmission, threshing unit, traveling unit, detecting unit, and controller. Each components of the system were mathematically modeled. By a computer simulation, the effects of control parameters such as hydraulic piston speed, speed ratio, dead band of engine speed on the system performance were analysed, and the optimum control conditions were identified. The system appeared to be the most stable at the hydraulic piston speed of 10.6mm/s and the speed ratio of 0.4. The proper dead band of engine speed appeared to be 30rpm through the simulation and verification tests.