• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.345-347
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• 1993

It is known that Feedback linearization has important limitations-the full state has to be measured; no robustness is guaranteed with respect to parameter uncertainty and unmodeled dynamics. In this paper, we construct a nonlinear feedback linearization controller for the system containing uncertain parameters and unknown states, in the case of EMS system with rail vibration. Performance of this controller is demonstrated by computer simulation.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.335-337
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• 2024

This paper presents a new open-world object detection method emphasizing uncertainty representation in machine learning models. The focus is on adapting to real-world uncertainties, incrementally updating the model's knowledge repository for dynamic scenarios. Applications like autonomous vehicles benefit from improved multi-class classification accuracy. The paper reviews challenges in existing methodologies, stressing the need for universal detectors capable of handling unknown classes. Future directions propose collaboration, integration of language models, to improve the adaptability and applicability of open-world object detection.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.173-176
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• 1996

This paper deals with the problem of designing an adaptive regulator in order to improve transient performance in time-response when the linear state-space model of the plant contains unknown parameters which vary within prescribed bounds. The whole possible parameter space is divided into some subspaces and multiple models and controllers are established from the view point that each controller gives satisfactory transient behavior for systems corresponding to each parameter subspace. Based on time-response and an associated cost function, an appropriate controller is selected on-line out of multiple controllers.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.359-363
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• 1994

In this paper the system called the inverse model compensation system is proposed as a system whose input-output transfer function can be regarded as that of a model with uncertainty in spite of including an unknown plant. And their to construct the robust model following system, which is of low sensitivity and robust stability, in order to control the inverse model compensation system is proposed. The simulation experiments show that the robust model following system including the inverse model compensation system is practical and useful as a system which controls unknown plants.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.315-322
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• 2004

A direct adaptive state-feedback controller for highly nonlinear systems is proposed. This paper considers uncertain or ill-defined nonaffine nonlinear systems and employs a static fuzzy logic system (FLS). The employed FLS estimates. and adaptively cancels an unknown plant nonlinearity using its proved universal approximation property. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The tracking error is guaranteed to be uniformly asymptotically stable rather than uniformly ultimately bounded with the aid of an additional robustifying control term. No a priori knowledge of an upper bound on an lumped uncertainty is required.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.930-932
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• 1996

Some results and a nonlinear controller are proposed for feedback linearizable SISO systems with unknown constant parameters. It is shown that the systems which satisfy the proposed conditions can be transformed into a controllable linear subsystem with unknown parameter and it can be stabilized using the high gain nonlinear feedback linearizing controller. As an example for the proposed theorem, we introduce the single link robot with joint flexibility which is an well known example.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1134-1136
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• 1996

A necessary and suficient conditions is proposed for feedback linearizable SISO systems with unknown constant parameters. It is shown that the systems which satisfy the proposed conditions can be transformed into a controllable linear system with unknown parameter and it can be stabilized using the nonlinear feedback linearizing controller. We also present the analysis and implementation of a nonlinear feedback linearizing control for an Electro-Magnetic Suspension (EMS) system. We show that an EMS system is nonlinear feedback linearizable and satisfies the proposed conditions, and hence that the proposed nonlinear feedback controller for an EMS system is robust against mass parameter perturbation and force disturbance.