• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.574-578
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• 1998

This paper considers the robust positive real problem for linear systems with linear fractional-type norm-bounded repeated scalar block parameter uncertainty. It is shown that the robust positive real problem can be converted into the standard positive real problem without uncertainty that can be used for the analysis of the given uncertain linear system and the synthesis of a controller that robustly stabilizes and achieves the extended strict positive realness property of the closed-loop transfer function. These results can be also applied to the linear system with general structured uncertainty containing repeated scalar block parameters and are extensions of the previous works that consider only norm-boundedness of the affine unstructured uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.472-475
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• 1997

The principle objective of this paper is to explain and demonstrate the advantage of the output-feedback controller proposed by Ailon in [61 by using simulation and experimental results. Namely, the goal of this study is to design and implement a real-time controller for set-point regulation of a one-link rigid robot manipulator with unknown parameters using only position measurement. For implementation a direct drive one-link rigid robot manipulator is constructed and a TMS320C40 DSP systems board is used in implementing real-time control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.425-430
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• 1991

For satellite orbit determination, a satellite (K-3H) which is affected by the earth's gravitational field and the earth's atmospheric drag, the sun, and the moon is chosen as a dynamic model. The state vector include orbit parameters, uncertain parameters associated with perturbations and tracking stations. These perturbations include gravitational constant, atmospheric drag, and jonal harmonics due to the earth nonsphericity. Early orbit was obtained with given the predicted orbital parameter of the satellite. And orbit determination, which is applied to Extended Kalman Filter(EKF) for real time implementation , use the observation data which is given by satellite tracking radar system and then orbit estimation is accomplished. As a result, extended sequential estimation algorithm has a fast convergence and also indicate effectiveness for real time operation.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.866-871
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• 1991

The multi-layer neural network possesses the desirable characteristics of parallel distributed processing and learning capacity, by which the uncertain variation of the parameters in the dynamically complex system can be handled adoptively. However the error back propagation algorithm that has been utilized popularly in the learning procedure of the mulfi-Jayer neural network has the significant limitations in the real application because of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.426-426
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• 2000

When real robot manipulators arc mathematically modeled, uncertainties are not avoidable. The uncertainties are often nonlinear and time varying, The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance and etc. We proposed a class of robust hybrid position/force control of manipulators and provided the stability analysis in the previous work. In the work, we propose a class of adaptive robust hybrid position/force control of manipulators with bound estimation and the stability based on Lyapunov function is presented. Especially, this controller does not need the information of uncertainty bound. The simulation results are provided to show the effectiveness of the algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.934-940
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• 1991

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergencs speed. In this paper, an approach to improve the convergence speed is proposed using fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.642-647
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• 1993

In general, systems contain uncertain elements in the real world; these may be parameters, constant or varying, that are unknown or imperfectly known. When the uncertainty is assumed to satisfy the matching condition and to be cone-bounded, Y.H. Chen[81 proposed an adaptive robust control algorithm which introduced adaptive scheme for a design parameter into robust deterministic controls. In this paper, the above control algorithm is applied to the position tracking control of 2 DOF direct drive SCARA robots, and simulation and experimental studies are conducted to verify the control algorithm and to evaluate control performance.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1133-1138
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• 1991

A control of nonlinear system is motivated by the fact that all real plants are nonlinear systems and model identification introduces parameter errors. The purpose of this study is to design a Discrete Variable Structure Controller(DVSC) for single-rod hydraulic cylinder system. The model contains uncertain parameters which we known to lie upper and lower bounds. In the design of DVSC, the boundary layer concept was adopted to reduce cattering. The DVSC was evaluated through digital computer simulation and compared with a VSC (analog controller).

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.517-522
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• 2000

When a real robot manipulator is mathematically modeled. uncertainties are not avoidable. The uncertainties are often nonlinear and time-varying. The uncertain factors collie from imperfect knowledge ok system parameters. payload change. friction. external disturbance. and etc. In this paper. we propose a class of robust hybrid controls of manipulators without knowing the exact stiffness and provide the stability analysis. Simulation results are provided to show the effectiveness of the algorithms.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.61-70
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• 2000

An Adaptive Positive Position Feedback method is presented for controlling the roll of the supersonic smart projectile. The proposed strategy combines the attractive attributes of Positive Position Feedback(PPF) of Goh and Caughey, and Lyapunov stability theorem. The parameters of Adaptive-PFF controller are adjusted in an adaptive mauler in order to follow the performance of an optimal reference model. In this way, optimal damping and zero steady-state errors can be achieved even in the presence of uncertain or changing plant parameters. The performance obtained with the Adaptive-PPF algorithm is compared with conventional PPF control algorithm. The results obtained emphasize the potential of Adaptive-PPF algorithm as an efficient means for controlling plants such as supersonic flight systems with uncertainties in real time.