• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.1
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• pp.14-21
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• 2001

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. In indirect adaptive fuzzy control, based on the proved approximation capability of fuzzy systems, they are used to capture the unknown nonlinearities of the plant. Until now, most of the papers in the field of controller design for nonlinear system considers the affine system using fuzzy systems which have fixed grid-rule structure. We proposes a dynamic fuzzy rule insertion scheme where fuzzy rule-base grows as time goes on. With this method, the dynamic order of the controller reduces dramatically and an appropriate number of fuzzy rules are found on-line. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed-loop system is guaranteed.

• Journal of the Korea Convergence Society
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• v.5 no.4
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• pp.163-169
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• 2014

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.728-730
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• 1999

This paper suggests a new supervisory control input for improving response of nonlinear system with adaptive neural controller. The proposed control input is constructed using variable parameters which is adjusted by a separated control law. With the proposed algorithm, the information about the parameters are not required and error range is managed by designer in this scheme. The effectiveness of the proposed control scheme is demonstrated through the control of inverted pendulum.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.296-298
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• 1993

The performance of MODEL-BASED controller is influenced by model parameter errors and velocity measurement errors. To reduce this errors, Control methods by parameter adaptation and velocity estimation are studied. But because these controller has complex construction and need much computation time, the implementation of single processor system is difficult. This paper proposes a control scheme which combines an adaptive control law with a sliding observer using transputer network.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.239-240
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• 2007

In this paper, a robust adaptive backstepping controller will be proposed for the speed control of permanent magnet synchronous motors in using electrical vehicles. Stator resistance, damping coefficient, load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the Proposed controller.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.3
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• pp.34-40
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• 2012

A pressure control law to regulate pressure of gas generator is suggested. To design a model based control law, the governing equation which consists of Robert and conservation equation is built and verified through the ground burning test. PID and nonlinear adaptive control laws are designed to analyze the loop response characteristics under the system which has varying eigen properties arisen from combustion volume change. It is suggested that new approach, gain scheduling design, is required to overcome the defects identified from numerical simulation results of the two control laws. The newly suggested scheme shows good control performance even under disturbances and measurement noise.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.618-623
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• 2011

A pressure control law to regulate pressure of gas generator is suggested. To design a model based control law, the governing equation which is consisted of Robert and conservation equation is built and verified through the ground burning test. PID and nonlinear adaptive control laws are designed to evaluate the loop response characteristics under the system which has varying eigen properties as combustor volume is increased. It is suggested that new approach, gain scheduling design, is required to overcome the defects identified from numerical simulation results of the two control laws. The newly suggested scheme showed good control performance even under disturbances and measurement noise.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.256-257
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• 2018

This paper deals with the path-following control for marine surface vehicles with underactuated characteristics. In consideration of practical limitations of actuators, an improved DVS(dynamic virtual ship) guidance algorithm is proposed with the multi-level DVS optionally selected to be tracked. To address the output-feedback control issue, an adaptive FLS(fuzzy logical systems) is devised to online approximate the kinematic states. Based on that observing framework, the path-following control law is thereafter derived. Simulations testify effectiveness of the proposed scheme

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.905-912
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• 1989

An adaptive control scheme is presented for uncertain systems whose uncertainties are upper-bounded by a linear combination of unknown constants and known continuous functions. The state of the closed-loop system is proven to be ultimately bounded. The proposed method modifies the method of Corless and Leitmann in the following two respects. First, the linear region of the saturation function in controller is fixed. Second, the intergration from in parameter estimator is replaced by a low pass filter form. These modifications prevent performance degradation and destabilization of the control system more effectively. The norm of the system states can be made sufficiently small by an appropriate choice of design parameters in the control law. The applicability of the proposed scheme is demonstrated in the position control of a simple pendulum via simulation.