• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.77-80
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• 1996

In this paper a nonlinear control strategy via feedback linearization and energy based Lyapunov function for underactuated mechanical systems is investigated. Underactuated mechanical system is a system of which the number of actuators is less than the number of degrees of freedom. Developed algorithm is applied to a crane system of grab operation. Positioning of the trolley as well as swing-up of the pendulum to the up-right position including maintaining the sway angle at some desired degree are demonstrated. Simulations are provided.

• Journal of Mechanical Science and Technology
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• v.18 no.2
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• pp.203-210
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• 2004

Attitude control law design for spacecraft large angle maneuvers is investigated in this paper. The feedback linearization technique is applied to the design of a nonlinear tracking control law. The output function to be tracked is the quaternion attitude parameter. The designed control law turns out to be a combination of attitude and attitude rate tracking commands. The attitude-only output function, therefore, leads to a stable closed-loop system following the given reference trajectory. The principal advantage of the proposed method is that it is relatively easy to produce reference trajectories and associated controller.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.23-30
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• 2009

This paper addresses how to make a formation of multiple unmanned aerial vehicles (UAVs) using only the relative range information. Since the relative range can easily be measured by an on-board range sensor like the laser range finder, the proposed method does not require any expensive and heavy wireless communication system to share the navigation information of each vehicle. Based on the two-dimensional (2-D) nonlinear equations of motion, we propose a nonlinear formation controller using the typical input-output feedback linearization method. The performance of the proposed formation controller is verified by various numerical simulations.

• 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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.6
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• pp.1036-1055
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• 1992

In this paper the feedback linearization of the valve-controlled nonlinear hydraulic velocity control system and the implementation of the digital state feedback controller is studied. The $C^{\infty}$ nonlinear transfomation to the electro-hydraulic velocity control system, which transforms nonlinear system to linear equivalent one, is obtained. It is shown that this transformation is global one. The digital controller to this linearized model is obtained by using the one-step ahead state estimator and implemented to real plant. The proposed implementation method is easier than the other proposed methods and it is possible to control in real time. The experiment and simulation study show that the implementation of the digital state feedback controller based on the feedback linearized model is successful..

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.12
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• pp.19-26
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• 1991

In this paper, we give a structure algorithm for the synthesis problems of the nonlinear system via dynamic feedback. Using our algorithm, sufficient conditions for the input-output synthesis problems are discussed. The problems we consider in this paper include dynamic input-output decoupling input-output linearization, and immersion into a linear system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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• pp.690-695
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• 1991

The ability to linearize a nonlinear system by feedback and coordinate change reduces to finding an integrating factor for a one-form which is determined from the system dynamics. Utilizing Taylor series expansion of this one-form, we characterize approximate linearizabilitu. A constructive method is derived for approximate linearization up to order 2.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.970-972
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• 1998

In this paper, the standard Dole, Glover, Khargoneker, and Francis (abbr. : DGKF 1989) $H_{\infty}$ controller $(H_{\infty}C)$ is extended to the nonlinear feedback linearization-$H_{\infty}$ /sliding mode controller (NFL-$H_{\infty}$/SMC) to solve the problem associated with the full state feedback for the unmeasurable state variables in the conventional SMC, to obtain the smooth control as the linearized controller for a linear system (or to cancel the nonlinearity for the nonlinear system), and to improve the time-domain performance under worst case.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.18-18
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• 2000

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 are 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. 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 shows 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