• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.271-276
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• 1994

In this paper we deal with a design of CCV adaptive flight control system having adaptive observer under the mircroburst circumstances. First, based on the observerbility indices of the controlled system, which is a general multi-variable one, the adaptive observer is constructed, and the unknown interactor matrix can be estimated by using the identified parameters. Next, CCV adaptive flight control law is calculated based upon the estimated ones. Finally, the proposed CCV adaptive flight controller is applied to STOL flying boat and numerical simulations under the microburst circumstances can be show to justify the proposed scheme.

• Proceedings of the KIEE Conference
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• 1983.07a
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• pp.135-138
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• 1983

This paper studies a variable structure adaptive model following control system which can control a plant in which the parameters of the controlled plant can not be estimated because they vary with time and in which the controlled plant has noise. The values of the feedback gain matrices for given states are obtained the equivalent control law, and the adaptive controller has been designed using the adaptive mechanism which switches the matrices. The adaptive controller minimizes the state error vector, that is, the difference between the state vector of the model and the state vector of the controlled plant. A controlled plant which has time varying parameters, a controlled plant which has only noise, and a controlled plant which has both have been controlled by the designed adaptive controller. The continuous single input-output system has been analysed by computer. This control system may be used to control practical systems by the addition of a microcomputer.

• Journal of Ocean Engineering and Technology
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• v.14 no.2
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• pp.7-12
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• 2000

This paper presents a non-regressor based adaptive control scheme for the trajectory tracking of underwater vehicle-mounted manipulator systems(UVMS). The adaptive control system includes a class of unmodeled effects is applied to the trajectory control of an UVMS. The only information required to implement this scheme ios the upper bound and lowe bound of the system parameter matrices the upper bound of unmodeled effects the number of joints the position and attitude of the vehicle and trajectory commands. The adaptive control law estimates control gains defined by the combinations of the bounded constants of system parameter matrices and of a filtered error equation. To evaluate the performance of the non-regressor based adaptive controller computer simulation was performed with a two-link planar robot model mounted on an underwater vehicle. The hydrodynamic effects acting on the manipulator are included. It is assumed that the vehicle's motion is slow and can be predicted with a proper compensator.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.1-14
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• 2004

This paper surveys some existing direct adaptive feedback control schemes for linear time-invariant systems with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed or varying values at unknown time instants, and applications of those schemes to aircraft flight control system models. Controller structures, plant-model matching conditions, and adaptive laws to update controller parameters are investigated for the following cases for continuous-time systems: state tracking using state feed-back, output tracking using state feedback, and output tracking using output feedback. In addition, a discrete-time output tracking design using output feedback is presented. Robustness of this design with respect to unmodeled dynamics and disturbances is addressed using a modified robust adaptive law.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.267-274
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• 1999

This paper proposes an adaptive sliding mode control scheme for an autopilot design of Skid-to-Turn (STT) missiles. The feedback linearization controller eliminates nonlinear terms in STT dynamics and makes the entire system linear. But the modeling errors in dynamics and the external disturbances exert bad influence on the performance of the feedback linearization controller. To handle these uncertainties, an adaptive control scheme is developed, where a bound of the uncertainties is estimated by an adaptive law based on a sliding surface. The asymptotic output tracking is proved by using the Lyapunov stability theory. Simulations for STT missiles illustrate the validity of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.708-712
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• 1991

This paper presents a new adaptive structure with fuzzy logic applied to adaptive controller. In the conventional adaptive control, good performance cannot be expected due to the adaptation gain of gradient algorithm fined as a constant. To change adaptation gain property, fuzzy rules, which are based on the output error and its rate of change, have been established. The proposed fuzzy adaptive law shows fast parameter convergence and improved performance. The fuzzy logic base is added to the conventional adaptive structure and little additional computation time is required.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.533-538
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• 2002

We propose a direct adaptive controller for a class of nonlinearly parametrized systems. A normalized output error is added to the previous control law to construct the proposed adaptive controller. The transient performance of the resulting closed-loop system can be guaranteed by suitably choosing a novel kind of Lyapunov function while the tracking performance of the adaptive control system is improved using the proposed algorithm. Finally, the effectiveness of the proposed algorithm is illustrated with computer simulation.

• 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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• 1992.10b
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• pp.135-139
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• 1992

Control of tool-environment interaction force to comply the robot system to an environment is of vital in many automated process. This paper presents the implementation of an adaptive force control with commercial robot system in two dimensional contour following task. A model reference adaptive control system, combined with the linear compensators, is implemented. That is, a use of adaptive control is to provide an auxiliary control system so that the contour following performance can be improved from that of using linear control system only. Hyperstability is used to derive the adaptive control law. Experimental verification of the proposed control system is obtained using PUMA 560 robot system. Data obtained experimentally shows that the use of additional adaptive control system improves the contour following performance about 30% in RMS contact force errors upon that of the system controlled by the linear compensators only.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.10
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• pp.555-562
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• 2000

We propose an adaptive nonlinear control algorithm for compensation of the stick-slip friction in a dynamic system. The friction force and mass of the system are estimated and compensated by adaptive control law. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Laypunov stability theory, and its convergence is guaranteed under the bounded noise or torque disturbance. We verified the performance of the proposed algorithm by computer simulation on one-DOF mechanical system with friction.