• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.368-371
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• 1996

In this paper, a feedforward-plus-feedback control scheme is presented to prevent congestion in store-and-forward packet switching networks. The control scheme consists of two algorithms. Specifically, the input traffic adjustment algorithm employs a fairness policy such that the transmission rate of the input traffic is proportional to its offered rate. The control signal computation algorithms to ensure stability of the overall system in the robust sense and to ensure the desired transient behavior in the adaptive, with respect to variations of input traffic, are designed.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.2
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• pp.157-163
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• 2014

In this paper, a robust MRAC (model reference adaptive control) scheme is applied to control an electrohydraulic positioning system under various loads. The inverse dead-zone compensator in the control system cancels out the dead-zone response, and an integrator added to the controller provides good position-tracking ability. LQG/LTR (linear quadratic Gaussian control with loop transfer recovery) closed-loop model is used as the reference model for learning the MRAC system. LQG/LTR provides a systematic technique to design the linear controller that optimizes the objective function using some compromise between the control effort and the system performance in the frequency domain. Different external load tests are performed to investigate the effectiveness of the designed MRAC system in real time. The experimental results show that the tracking performance of the proposed system is highly accurate, which offers considerable robustness even with a large change in the load.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1895-1902
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• 2001

This paper gives an overview of the relationships between methods of loaming and adaptive control. It is the objective of this paper to develop adaptive learning control algorithms that combine the advantages of adaptive control with those of leaning control to the extent possible for the type of system model used. The robustness of this adaptive loaming control with respect to reinitialization errors and fluctuation of dynamics from disturbance is analyzed extensively. Simulation results have shown to verify the effectiveness of the proposed control algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.472-477
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• 1996

This paper presents a new approachtothe design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The prpposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Power Electronics
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• v.6 no.4
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• pp.347-355
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• 2006

A new control method for precision robust position control of a permanent magnet synchronous motor (PMSM) is presented. In direct drive motor systems, a load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in using a fixed gain to solve this problem. However, the motor flux linkage cannot be determined precisely for a load torque observer. Therefore, an asymptotically stable adaptive observer base on a deadbeat observer is considered to overcome the problems of unknown parameters, torque disturbance and a small chattering effect. To find the critical parameters the system stability analysis is carried out using the Liapunov stability theorem.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.270-276
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• 2014

This survey paper reviews robust control problems in both frequency domain and time domain. Robust control is focused on model uncertainties such as modeling error, system parameter variations, and disturbances. Robust control design problems are discussed according to parameter uncertainty, polytopic uncertainty, and norm-bounded uncertainty. Nowadays, robust control theory is combined with various control theory such as model predictive control, adaptive control, intelligent control, and time delay control.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.636-639
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• 1997

In this paper, a dynamic control scheme is proposed which not only compensates for the lateral dynamics and longitudinal dynamics but also deal with the yaw motion dynamics. Using the dynamic control technique, adaptive and learning algorithm together, the proposed controller is not only robust to disturbance and parameter uncertainties but also can learn the inverse dynamics model in steady state. Based on the proposed dynamic control scheme, a dynamic vehicle simulator is contructed to design and test various control techniques for 4-wheel steering vehicles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.7
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• pp.1414-1420
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• 1994

In this paper the existence of chaotic signal is probed in adaptive dead beat control law for nonlinear dynamic system. These chaotic signal makes the system unstable and difficult to control, but it broaden the range of application, confirms the robustness of system and gives a lot of information. Considering of low correlation between chaotic signals, robust adaptive control method which uses for parameter estimation is proposed. With this algorithm the parameters converges stable rapidly. Finally the superiority of it is proved by computer simulation.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.127-134
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• 2008

In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.261-269
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• 2014

This paper provides an overview of the basics and recent studies of Lyapunov-based nonlinear adaptive control, the aim of which is to improve or maintain the performance and stability of the closed-loop system by cancelling out the presumable uncertainties in the nonlinear system dynamics. The design principles are essentially based on Lyapunov's direct method. In this survey, we provide a comprehensive overview of Lyapunov-based nonlinear adaptive control techniques with simplified effective design examples, which are to be elaborated as related recent results are gradually shown. The scope of the survey contains research on singularity problems in adaptive control, the techniques to deal with linearly and nonlinearly parameterized uncertainties, robust neuro-adaptive control, and adaptive control methodologies combined with various nonlinear control techniques such as sliding-mode control, back-stepping, dynamic surface control, and optimal/$H_{\infty}$ control.