• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.332-335
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• 1997

In the industrial motor drive system, a shift torsional vibration si often generated when a motor and a load are connected with a flexible shaft. This paper treats the vibration suppression control of such a system. In this paper, the state feedback controller of the two-mass resonant system using the H$\infty$ filter is proposed. The H$\infty$ filter is robust in noise and disturbance. Simulation results show the validity proposed controller.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.273-275
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• 1992

A dynamic system containing uncertain elements is considered. The upper bound of the values of these uncertainties is estimated. Using the estimated value a bounded error state feedback control is proposed based on Corless and Leitmann's result [1].

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.943-951
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• 1995

In this paper the projective controls, previously derived to preserve the dynamic modes of a state-feedback reference system, are extended to allow the preservation of the modes of a general output-feedback reference system. In general, the extension allows projective controls to be used as a controller approximation technique, where a reduced-order controller is designed to approximate the closed-loop behavior of the higher-order reference controller. This extension is useful if the best available reference control for the system is an output-feedback control. An example shows that the increased design freedom of proposed design method allows the stabilization of a given plant using a lower-order controller than the projective controls with state-feedback reference.

• Journal of Industrial Technology
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• v.18
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• pp.303-308
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• 1998

In this paper a neural network controller called "Feedback-State Learning" for control of the attitude of a wheeled inverted pendulum is presented. For the controller the design of a stable feedback controller is necessary, so the LQR is used for the feedback controller because the LQR has good performance on controlling nonlinear systems. And the neural networks are used for a feed forward controller. The designed controller is applied to the stabilization of a wheeled inverted pendulum. Because of its nonlinear characteristics such as friction and parameter variations in the linearization, the wheeled inverted pendulum is used for demonstration of the effectiveness of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.444-448
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• 2003

In this paper, the Lagrange dynamics is studied. A state space representation of Lagrange dynamics and control algorithm based on the state feedback pole placement are presented. The state space model presented is descriptor type linear parameter dependent system. It is shown that the control algorithms based on the linear system theory can be applicable to the state space representation of Lagrange dynamics. To show that the linear system theory can be applicable to the state space representation of Lagrange dynamics, the LMI based regional pole-placement design algorithm is developed and present two examples.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.92-94
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• 2007

This paper considers an optimal control problem for a remote control to an electrical drive system with a DC motor. Since it is a linear control system with time-delay subject to unknown but bounded disturbance, we construct a worst-case feedback control policy. This policy can guarantee that, for all admissible uncertain disturbances, the real system state should be in a prescribed neighborhood of a desired value, and the cost functional takes the best guarantee value. The worst-case feedback control policy is allowed to be corrected at one correction point between the initial to the final time, which is equivalent to solving a 1-level min-max problem. Since the min-max problem at the stage does not yield a simple analytical solution, we consider an approximate control policy, which is equivalent and can be solved explicitly m the numerical experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.494-499
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• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.907-912
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• 1989

A new procedure is presented for optimally placing closed-loop poles of multivariable continuous-time systems in specified regions via linear-quadratic(LQ) state-feedback design. This method has the advantages of pole-placement and LQ-design. In addition, it provides minimum feedback gains in the control law.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1400-1406
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• 2016

This paper presents a new adaptive converter control approach for electric motor systems whose voltage source is excited from photovoltaic (PV) power generators. First, an electric model is represented with dynamic states and output velocity of such DC motor systems. We propose a hybrid converter control law in which a state feedback control is applied as an auxiliary control framework. Moreover, control parameter estimation is derived to realize adaptive converter systems for effective control performance against stochastic PV power excitation in practice. We carry out stability analysis for such converter system by using a well-known eigenvalue theory. Lastly, numerical simulation is conducted to test reliability of the proposed converter control approach and prove its superiority in the control point of view.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.819-832
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• 2017

We propose a new switching control scheme for a ball and beam system by utilizing two linearization methods. First, the Jacobian linearization is applied and state observer is developed afterward. Then, motivated [6], the approximate input-output linearization is carried out, and after that, the Jacobian linearization is applied along with the design of state observer. Since the second approach requires two linearizations, it is called a two-step linearization method. The state observer is needed for the estimation of the velocities of ball and motor movement. Since the Jacobian linearization based controller tends to provide faster response at the initial time, and after that, the two-step linearization based controller tends to provide better response in terms of output overshoot and convergence to the origin, it is natural to give a switching control scheme to provide the best overall control response. The validity of our control scheme is shown in both simulation and experimental results.