• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.548-552
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• 1994

The robust compensation controller, which has been proposed by one of the authors and is based on the fundamental principle of making the plant follow the reference model, consists of the reference model and the robust compensator. The reference model is constructed by using the nominal model of the plant and determines the input-output properties of the resultant system. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore the resultant system is of low sensitivity and robust stability. In the case where uncertainty does not occur in the plant, the plant follows perfectly the reference model. Therefore, in the case where uncertainty occurs in the plant, we propose the system configuration which improves the following accuracy without replacing the 개bust compensator but by identifying, the plant and reconstructing the reference model.

• 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
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• v.59 no.1
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• pp.160-167
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• 2010

This paper proposes an adaptive sliding mode observer (SMO), which adds the estimation function of the stator resistance to a new sliding mode observer for the robust sensorless control of permanent magnet synchronous motor (PMSM) with variable parameters. To reduce the chattering problem commonly found in the conventional sliding mode observer where the low-pass filter and additional position compensation of the rotor are used, the sigmoid function is used for the control of a switching function in this research. With the estimation of the stator resistance, the proposed observer can improve the control performance by reducing the estimation error of the motor's speed. Note that the stator resistance is varying with the ambient temperature and becomes an error source for the sensorless control of PMSM. The new sliding mode observer has better efficiency than the conventional adaptive sliding mode observer by reducing the time consuming integral calculations. The stability of the proposed adaptive sliding mode observer is verified by the Lyapunov function in determining the observer gains, and the effectiveness of the observer is demonstrated by simulations and experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1005-1013
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• 2009

In this paper, the performance improvement of a decentralized brake system for railway rolling stocks is investigated. In order to verify the effectiveness of the decentralized brake system, it is compared to the truck unit brake system which has only one control unit per a truck. The adaptive sliding mode control scheme is used to realize a robust anti-slip brake control system. Through computer simulations, it is verified that the decentralized brake system has better braking performance than the truck unit brake system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.562-567
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• 2000

This paper proposed a speed estimator using MRAC(Model Reference Adaptive Control) for sensorless vector control. It is robust for parameter variation and the estimated speed is used as feedback in a vector control system. Computer simulation is presented to confirm the theoretical analysis.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.177-182
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• 2003

The robust output tracking control problem of general nonlinear MIMO systems is discussed. The robustness against parameter uncertainties is considered. In this paper, we proposed the robust output tracking control scheme for a class of MIMO nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r$_{1}$＋r$_{2}$＋…r$_{m}$$\leq$ n and zero dynamics is stable. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. And we verified that the proposed control scheme is then applied to the control of a two degree of freedom (DOF) robotic manipulator with payload.d.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.615-617
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• 1999

In this paper, we present a decentralized robust adaptive fuzzy controller for the transient stability and voltage regulation of a multimachine power system under a sudden fault. Power systems have uncertain dynamics due to various effects such as lightning, severe storms and equipment failure in addition to interconnections between generators. Hence a robust controller to deal with these uncertainties is needed. Simulation results show that satisfactory performance is achieved by the proposed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.199-203
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• 1997

In this paper is proposed a new direct adaptive fuzzy controller that dan ve applied for tracking control of a class of uncertain nonlinear SISO systems. It is shown that, in the presence of the perturbations such as fuzzy approximation error and external disturbance, boundedness of all the signals in the system is ensured, while under the assumption of no perturbations, the stability of the overall system in guaranteed. Also, the concept of persistent excitation in the adaptive fuzzy control systems is introduced to guarantee the convergence and the boundedness of adaptation parameter in the proposed controllers. Simulation example shows the effectiveness of the proposed method in the presence of fuzzy approximation error and external disturbance.

• Advances in Computational Design
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• v.5 no.3
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.