• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.342-345
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• 1989

In this paper, a controller design for a electro-hydraulic servo system is presented. When state variables of the system are not directly measurable for feedback control, it is very difficult to satisfy the given requirements for the system output control. The proposed design method is based on the feeding back of the output variable and it's time delayed values.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.176.4-176
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• 2001

In this paper, a novel type of iterative learning controller is studied. The proposed learning algorithm utilizes not only the error signal of the previous iteration but also the delayed error signal of the current iteration. The delayed error signal is adopted to improve the convergence speed. The convergence condition is examined and the result shows that the proposed learning algorithm shows the fast convergence speed under the same convergence condition of the traditional iterative learning algorithm. The simulation examples are presented to confirm the validity of the proposed ILC algorithm.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.24-34
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• 2008

A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.109-114
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• 1997

In this paper, we present an output feedback $H^\infty$controller design method and derive the sufficient condition of the bounded real lemma for linear systems with multiple delays in states. For state delayed systems, sufficient conditions for the existence $\kappa$-th order $H^\infty$controllers are given in terms of three linear matrix inequalities(LMIs). Furthermore, we show how to construct such controllers from the positive definite solutions of their LMIs and given an example to illustrate the validitiy of the proosed design procedure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.4
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• pp.342-349
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• 1991

In this paper, a method is presented for designing a tracking and disturbance rejecting controller for a nonlinear control system in which approximate linearization is not applicable due to a s stiff nonlinearity. Only the measurable variables are used for the controller synthesis. The system is augmented by a compensator at the output side for the tracking and disturbance rejection. An output delayed feedback controller is designed for the augmented system without nonlinearity. Then the feedback parameters are adjusted by describing function method to overcome the limit cycle due to the nonlinearity.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.161-167
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• 1993

This paper presents robustness properties of the Kalman Filter ad the associated LQG/LTR method for linear time-invariant systems having delays in both the state and output. A circle condition relating to the return difference matrix associated with the Kalman filter is derived. Using this circle condition, it is shown that the Kalman filter guarantees(1/2, .inf.) gain margin and .+-.60.deg. phase margin, which are the same as those for nondelay systems. However, it is shown that, even for minimum phase plants, the LQG/LTR method can not recover the target loop transfer function. Instead, an upper bound on the recovery error is obtained using an upper bound of the solution of the Kalman filter Riccati equations. Finally, some dual properties between output-delated system and input-delayed systems are exploited.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.463-468
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• 2002

In this paper, we proposed a recurrent time delayed neural network controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of learning a inverse model neural network of plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed recurrent time delayed neural network controller get a good response compare with a time delayed neural network controller.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.145-148
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• 1990

In this paper, the problem of designing a feedback controller for a robotic manipulator, which is activated by a D.C. motor through a gear train and a flexible shaft or chain, is considered. When the response of the closed loop control system is relatively slow, a satisfactory controller may be designed as a PID controller. As the speed of the control system increases, however, the spring effect of the linkage becomes profound, and as a result, the transient response exhibits a substantial oscillation. To eliminate this oscillation, it is necessary to design the controller based on at least a fourth order system model. This, in turn, requires the feedback of the entire state variables. In practice, however, only the position of the manipulator and the velocity of the motor are readily measurable. The state variable reconstruction method or a state observer cannot be used because of the system nonlinearities such as the Coulomb frictions. In this study, an alternative controller, which is based on delayed feedback of the output variable only, is proposed, and a successful delayed feedback controller is designed and implemented on an actual experimental manipulator.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.253-257
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• 2013

This paper proposes the robust observer design for nonlinear systems with delayed output and external disturbance. It is shown that by considering a nonlinear term of error dynamics as an additional state variable, the nonlinear error dynamics with time delay can be transformed into the linear one with time delay. Sufficient conditions for existence of a robust observer are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1234-1238
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• 2004

A Controlled neural networks are proposed in order to measure nonlinear environments in adaptive and in realtime. The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between tile output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by backpropagation and each weights are updated by RLS(Recursive Least Square). Consequently, this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. This new neural networks is Error Estimated Neural Networks. We can estimate nonlinear models in realtime by the proposed networks and control nonlinear models. To show the performance of this one, we have various experiments. And this controller call prove effectively to be control in the environments of various systems.