• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.22-29
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• 2004

This paper studies the coordinated trajectory control of an excavator as a kind of robotic manipulators driven by hydraulic actuators. Hydraulic robot system has many non-linearity in dynamics and kinematics, and strong coupling among joints(or hydraulic cylinders). This paper proposes a combined controller frame of the adaptive robust control(ARC) and the sliding mode control(SMC) for the trajectory tracking control of the excavator to preserve the advantages of the both methods while overcoming their drawbacks, namely, asymptotic stability of adaptive system for parametric uncertainties and guaranteed transient performance of sliding mode control for both parametric uncertainties and external disturbance. The suggested control technique is applied for the tracking of a straight-line motion of end-effector of manipulators, and through computer simulations, its trajectory tracking performances and the robustness to payload variation and uncertainties are illustrated.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.4
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• pp.259-266
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• 1987

In order to drive motor control system precisely, the motor is to be controlled by mmfs and current with sinusoidal waveforms. In this paper the Delta Modulation (DM) Technique is used for generating PWM pulse with sinusoidal waveform. However the motor currents yet contain odd harmonics due to leakage inductances, speed and exitation. To reduce harmonics, the parameter adaptive control method is introduced. That is, Req.C parameter of Delta Modulator is controlled adaptively by parameter adaptor. The adaptive signal is achieved by the difference between motor current and reference waveform, and this signal is converted to the voltage commend signal by adaptive mechanism. The test reslts show that this system is operated smoothly over a wide range of motor speed and motor current is controlled to be sinusoidal waveform adaptively.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.352-355
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• 1986

In this paper, some candidates suggested for the improvement of the robustness in adaptive control systems are shortly surveyed. Using dead zone concept of error in adaptation process, gain retardation methods such as .sigma.-modification and parameter restriction method are those considered. Feedforward compensation and normalized adaptation technique are also considered. New modeling technique suggested by Donati et al is used for the indirect control of plants containing unmodeled dynamics. The frequency band of input signal, which is used as a test signal and control signal simultaneously, is directly related to the control of plants containing high frequency parastics. Computer simulation results of the some selected algorithms are shown.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.425-426
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• 2006

This study proposes a new simulation method of high speed rotor system with the dynamic model using multi body dynamic analysis tool and with a new phase modulating technique as a system control algorithm. A dynamic model of high speed rotor system was built by, ADAMS, commercial multi body dynamic program. The phase modulating technique is a new control algorithm for a rotor system. This algorithm can control system using an adaptive proportional gain and an adaptive phase which are obtained from periodical input signal. To make control system, a ADAMS model and component parameters and phase controller was composed by Matlab Simulink And simulate it.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.5
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• pp.464-469
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• 2009

In this study, a new fault tolerant controller based on a model following adaptive technique is applied to the reconfiguration mode of supersonic advanced trainer. The designed controller is applied to the flight control system of high performance aircraft. To verify the performance of the proposed controller, numerical simulations are executed using a non-realtime nonlinear verification tool.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.174-180
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• 2012

This paper proposes a decentralized adaptive output-feedback controller design for nonlinear interconnected systems with unknown time delays. The interaction terms with unknown delays are related to all states of subsystems. The time-delayed functions are compensated by using appropriate Lyapunov-Krasovskii functionals and function approximation technique. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.720-723
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• 1997

The characteristics of ball and beam system using fuzzy inference technique can be described by fuzzy modeling. Therefore, this paper introduces a technique for fuzzy structure identification of nonlinear Input-output relation- ship using an adaptive fuzzy inference system. And the simulation result using adaptive fuzzy inference technique shows its effectiveness for fuzzy structure identification of nonlinear system.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.4
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• pp.37-43
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• 1993

As an approach to develope the intelligent control scheme, this paper will propose an adaptive neural-fuzzy control scheme. The proposed neural-fuzzy control system, which consists of the Fuzzy-Neural Controller(FNC) and Model Neural Network(MNN), has two important characteristics of adaptation and learning. The error back propagation algorithm has been adopted as a learning technique.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1500-1505
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• 2004

This paper presents the design and control of a small Brushless DC (BLDC) Motor for entertainment robots. In order to control the developed BLDC motor, Adaptive Fuzzy Control (AFC) scheme via Parallel distributed Compensation(PDC) is developed for the multi- input/multi-output plant model represented by the Takagi-Sugeno(TS) model. The alternative AFC scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain or slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop system. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal. The suggested design technique is applied to the velocity control of a developed small BLDC motor for entertainment robots.