• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.61-64
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS32OC50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator. In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters are determined by the pole-placement method. Performance of self-tuning adaptive controller is illustrated by the simulation and experiment for a SCARA robot.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1602-1607
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• 2003

Vibration causes noise and sometimes makes structure unstable. Especially, due to the efforts of lightening, deformation of flexible structure is increased in its shape. Just a little disturbance can cause vibration and low damping ratio makes residual vibration last long time. This research is concerned with the model reference neuro-controller design for the vibration suppression of smart structures. By using a model reference neurocontroller, which is one of the algorithms of adaptive control, we performed an adaptive control of flexible cantilever plate and opened box structure with piezoelectric materials. The proposed adaptive vibration control algorithm, a model reference neuro-controller, was proved in its effectiveness by applying to an opened box structure. The model reference neuro-controller is implemented with DSP, and the real-time adaptive vibration control experiment results confirm that the model reference neuro-controller is reliable.

• ETRI Journal
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• v.35 no.1
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• pp.27-34
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• 2013

This paper proposes a network-adaptive mechanism for HTTP-based video streaming over wireless/mobile networks. To provide adaptive video streaming over wireless/mobile networks, the proposed mechanism consists of a throughput estimation scheme in the time-variant wireless network environment and a video rate selection algorithm used to increase the streaming quality. The adaptive video streaming system with proposed modules is implemented using an open source multimedia framework and is validated over emulated wireless/mobile networks. The emulator helps to model and emulate network conditions based on data collected from actual experiments. The experiment results show that the proposed mechanism provides higher video quality than the existing system provides and a rate of video streaming almost void of freezing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1019-1028
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• 2007

In this paper, adaptive evolutionary algorithm(AEA) is proposed, which uses both genetic algorithm(GA) with good global search capability and evolution strategy(ES) with good local search capability in an adaptive manner, when population evolves to the next generation. In the reproduction procedure, proportion of the population for GA and ES is adaptively determined according to their fitness. The AEA is used to design membership functions and scaling factors of the fuzzy logic controller(FLC). To evaluate the performance of the proposed FLC design method, we make an experiment on the FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than PD controller.

• Journal of the Ergonomics Society of Korea
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• v.36 no.2
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• pp.145-156
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• 2017

Objective: This study aims to compare the usability of the adaptive touch design method with that of the height adjustable design method that are applied to the Korean QWERTY keyboard and Naratgul keyboard on smartphones, examine the results, and present practical implications. Background: Smartphone manufacturers have failed to satisfy every user with their uniform touch keyboard designs that do not consider the high use rates of keypad use. In reality, touch keyboard designing customized for every individual is impossible, but there need to be researches on was to improve usability by having touch areas changed automatically depending on user behaviors or having users adjust the keyboard height depending on their hand size. Method: As for the design methods, an object group was given smartphones with the adaptive touch design method and the other group those with the height adjustable design method. As they entered the same characters in the smartphones, typing error rates and text input speed were measured and the average values were compared. 35 individuals who would frequently use smartphones in daily life participated in the experiment. The group variable was the type of touch keyboards, and the test variables were typing error rates and text input speed, for which a T-test was implemented. Results: As for the QWERTY keyboard, the significant improvement effect was verified as the typing error rate of the adaptive touch design method was 4.21% but that of the height adjustable design method was 3.28% although there was no significant difference in terms of text input speed. As for the Naratgul keyboard, in contrast, the typing error rate of the adaptive touch design method was 2.5% while that of the height adjustable design method was 1.48%, which indicates a measure of improvement, but the effect was not significant. On the other hand, the text input speed per minute was improved as much as 22.2%, which is significant. Conclusion: First, the Korean touch keyboard usability of the adaptive touch design method and that of the height adjustable design method, when applied to Model A of Company L, showed significant difference from each other. Second, the height adjustable design method was applied to the QWERTY keyboard, the typing error rate was improved significantly. This indicates that as the keyboard height was raised, the number of buttons within the range of fingering decreased, decreasing the touch bias was reduced. Third, the height adjustable design method was applied to the Naratgul keyboard, the text input speed was improved. Application: When the QWERTY keyboard was applied to a smartphone as small as 5.5inch or less, it is highly probably that the height adjustable design method decreases the typing error rate. It may be considered to develop additional UX functions to make the keyboard font larger or give users the option to adjust button intervals in utilization of the SW advantages of the height adjustable design method.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.51-54
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• 2007

In this paper, the dynamic models of a SOFC are rearranged. It consists of electrochemical model, thermal model, voltage equation and several loss equations. Experiment results of the real SOFC system are shown to evaluate the steady voltage characteristics. Control problems on tracking steady voltage by air flow is discussed and an adaptive controller is designed to withstand to the variation of stack current. Simulation is done to prove the solution of control algorithms.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.13-18
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• 2000

In this paper, an Adaptive Evolutionary Computation(AEC) is proposed. AEC uses a genetic algorithm(GA) and an evolution strategy (ES) in an adaptive manner is order to take merits of two different evolutionary computations: global search capability of GA and local search capability of ES. In the reproduction procedure, proportions of the population by GA and ES are adaptively modulated according to the fitness. AEC is used to design the membership functions and the scaling factors of fuzzy logic controller (FLC). To evaluate the performances of the proposed FLC, we make an experiment on FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than that of PD controller.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.570-572
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• 2005

This paper is proposed adaptive hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.