• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.60.1-60
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• 2002

. an output feedback stabilizing controller for non-minimum phase nonlinear systems . Assumption 1 : the Jacobi linearization of the given nonlinear linear system is controllable . Assumption 2: an appropriate transformation which transforms the zero dynamics into a special form . Assumption 3: the system satisfies the observability rank condition . Augmentation of systems by augmented by a chain of integrators

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.405-412
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• 1992

The problem we consider in this paper is more demanding than the problem of input-output linearization with state equivalence recently solved by Cheng, Isidori, Respondek, and Tarn. We request that the MIMO nonlinear system, for which the problem of input-output linearization with state-equivalence is solvable, can be decoupled. In exchange for further requirement like this, our problem produces more usable and informative results than the problem of input-output linearization with state-equivalence. We present the necessary and sufficient conditions for our problem to be solvable. We characterize each of the nonlinear systems satisfying these conditions by a set of parameters which are invariant under the group action of state feedback and transformation. Using this set of parameters, we can determine directly the unique one, among the canonical forms of decouplable and controllable linear systems, to which a nonlinear system can be transformed via appropriate state feedback and transformation. Finally, we present the necessary and sufficient conditions for our problem to be solvable with internal stability, that is, for stable decoupling.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.333-338
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• 1993

A feedback linearization controller for EMS system is implemented using DSP. In this paper, we show that given EMS system is input-state linearizable and satisfies some robustness condition. Also we derive feedback linearization controller for given system. Finally, some experiments are performed to demonstrate the performance of the proposed controller-especially, comparing this with the classical state feedback controller using linear perturbation.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.644-647
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• 1997

Asymmetric cylinders are usually used as an actuator of active suspensions. The conventional optimal controller design does not include actuator dynamics as a state and force controller is needed to track the desired force. But the actuator is not ideal, so performance of an active suspension system is degraded. In this paper, we take account nonlinear actuator dynamics and obtain a linear model using a feedback linearization technique then apply optimal control method. Effectiveness of proposed method is demonstrated by numerical simulation of 1/4 car model.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.356-362
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• 2002

In this paper, well-known Takagi-Sugeno fuzzy model is used as the nonlinear plant model and uncertainty is assumed to be included in the model structure with known bounds. Based on the fuzzy models, a numerical robust stability analysis for the fuzzy feedback linearization regulator is presented using Linear Matrix Inequalities (LMI) Theory. For these structured uncertainty, the closed system can be cast into Lur'e system by simple transformation. From the LMI stability condition for Lur'e system, we can derive the robust stability condition for the fuzzy feedback linearization regulator based on Takagi-Sugeno fuzzy model. The effectiveness of the proposed analysis is illustrated by a simple example.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.6
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• pp.558-564
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• 2008

This paper presents an approach to combine adaptive controller with feedback linearization, which extends the applicability of the adaptive controller to a wider class of systems. The adaptive controller guarantees the asymptotic tracking convergence and the transient performance of the tracking error. The feedback linearization transforms a nonlinear plant into a linear time invariant form. The asymptotic tracking convergence is shown by the use of Lyapunov stability analysis and Barbalat's lemma.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.377-384
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• 2007

This paper studies on the relation between linearization methods and controller gains in the looper ILQ(lnverse Linear Quadratic optimal control) system for hot strip finishing mills. Firstly, two linear models arc respectively derived by a linearization method using Taylor's series expansion and a static state feedback linearization method, respectively, and the linear models are compared with the nonlinear model. Secondly, the looper servo controllers are respectively designed on the basis of two linearization models. Finally, the relation between the performances of two ILQ servo controllers and the linearization methods, and the structures and control gains of two controllers are evaluated by a computer simulation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.302-307
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• 2006

The aim of applying Functional Electrical Stimulation (FES) is to restore a person's motor function by directly supplying the controlled electrical currents to the site of the paralyzed muscles. However, most clinically utilized FES systems have adapted an open-loop control scheme. Recently the closed-loop control scheme has been considered for setting up the FES system, but due to the inherent nonlinearities in the musculoskeletal system, the nonlinearities were not fully compensated and it caused the oscillatory responses for tracking the output variables. In this study, a nonlinear controller model that has two inverse compensation units is proposed with the compromising feedback linearization method and this will eventually be used to design the FES control system for stimulating a knee joint musculoskeletal system.