• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.678-681
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• 1991

In this paper, we propose a robust linear time-invariant feedback compensator design methodology for multivariable system which have both matched and mismatched uncertainties. In order to attack the problem of designing robust compensators guaranteeing uniform ultimate boundedness of every closed-loop system response within an arbitrarily small ball centered at the zero state based solely on the knowledge of the upper norm-bounds of uncertainties, we use an approach based upon the comparison theorem which is an effective approach in studying augmented feedback control systems with both mismatched and matched uncertainties. Through the approach, we draw some sufficient conditions for robust stability, and we give a simple example.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.239-241
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• 2001

Recently the power supply equipments have tendency to take multiple feedback loop paths. In this paper, the state space averaging technique is applied for the analysis of flyback type current mode control circuit. We made real converter for the gurantee of stable output characteristic and proper design of feedback circuit. The validity of proposed method is verified from test results. The improvement of stability is confirmed by sinusoidal signal injection method with isolated transformer. It is known that phase margin is sufficient and gain crossover frequency $f_c$, is nearly 1/5 of switching frequency $f_s$, from the experimental result with frequency response analyzer.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.58-61
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• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.4
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• pp.189-197
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• 2003

The problem of designing a parallel feedforward compensator (PFC) is considered for a class of non-square linear systems such that the closed-loop system is strictly passive. If a given square system has (vector) relative degree one and is weakly minimum phase, the system can be rendered passive by a state feedback. However, when the system states are not always measurable and the given output is considered, passivation (i.e. rendering passive) of a non-minimum phase system or a system with high relative degree cannot be achieved by any other methodologies except by using a PFC. To passivate a non-square system we first determine a squaring gain matrix and design a PFC such that the composite system has relative degree one and is minimum phase. Then the system is rendered strictly passvie by a static output feedback law. Necessary and sufficient conditions for the existence of the PFC and the squaring gain matrix are given by the static output feedback formulation, which enables to utilize linear matrix inequality (LMI). As an application of the scheme, an alternative way of replacing the role of velocity measurements is provided for the PD-control law of a convey-crane system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.45-52
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• 2006

In this study, a nonlinear control by feedback linearization method, one of nonlinear control schemes based on the nonlinear model, is proposed to suppress the flutter of a supersonic composite panel using piezoelectric materials. Most of the previous panel flutter controllers are the LQR(Linear Quadratic Regulator) which is based on the linear model. A nonlinear feedback linearizing controller proposed in this study considers the nonlinear characteristics of the system model. We use the actuator implemented by piezoceramic PZT. Using the principle of virtual displacements and a finite element discretization with the conforming four-node rectangular element, we first derive the discretized dynamic equations of motion, which are transformed into a nonlinear coupled-modal equations of motion of state space form. The effectiveness of the proposed method is also compared with the LQR based on the linear model through numerical simulations in the time domain using the Newmark method.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.2
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• pp.25-33
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• 2010

Introducing the quantized channel state information (CSI), space division multiple access (SDMA) can extract the multiplexing gain with the limited feedback burden. However, huge signaling burden of feedback can still suffer SDMA system because the total feedback data of SDMA is linearly dependent on the number of users. Hence, we propose a new feedback scheme to control the feedback load decided by the number of users. In this scheme, the cut-off level, which restricts the feedbacks of poor conditioned users, is suggested for the reduction of the feedback burden without the performance loss. From simulation results, then, we show that the proposed feedback scheme can achieve not only the sum-rate gain but also the reasonable feedback reduction.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.544-549
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• 1990

In industrial multivariable plants, it is often the case that the plant outputs are detected not simultaneously but serially. In this paper, the problem of estimating the state vector of the plant based on the data obtained from such a detecting scheme is considered, and a special type of observer (referred to as a "serial-sampling' type observer) which renews its internal states whenever a new data is obtained is proposed. It is proved that such an observer can be constructed for almost every sampling period if the plant is observable as a continuous-time multivariable system, and that the poles of the closed-loop system using the serial-sampling type observer consist of the poles of the observer and those of the state feedback system. The behaviors of the observer and the closed-loop system are studied by simulation. The results of simulation indicate that a serial-sampling type observer can estimate the state of the plant more accurately than the ordinary type observers and improve the closed-loop performance.ance.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.275-280
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• 2005

The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.48-51
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• 1996

This note considers the $H^{\infty}$ controller design problem for linear systems with time-varying delays in states. We obtain sufficient conditions for the existence of k-th order $H^{\infty}$ controllers in terms of three linear matrix ineualities(LMIs). These sufficient conditions are dependent on the maximum value of the time derivative of time-varying delay. Furthermore, we briefly explain how to construct such controllers from the positive definite solutions of their LMIs and give an example.e.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.