• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.363-368
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• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

• International Journal of Control, Automation, and Systems
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• v.4 no.1
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• pp.124-135
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• 2006

In this paper, we propose a switching control method for a class of 2nd order nonlinear systems with single input. The main idea is to switch the control law before the trajectory of the solution arrives at singular hyperplanes which are defined by the denominator of the control law. The proposed method can handle a class of nonlinear systems which is difficult to be stabilized by the existing methods such as feedback linearization, backstepping, control Lyapunov function, and sliding mode control.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.122-124
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• 2005

In this paper, we propose a switching control method for 2nd order nonlinear systems. The main idea behind the method is changing the control law before the trajectory of the solution arrives at the singularities imposed on the denominator of the control law. We show that the control system is asymptotically stable from the fact that the sector consisting of the singular hyperplanes is an invariant set. Illustrative examples are given.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.451-454
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• 1998

This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.395-397
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• 2007

In this paper, a Gauss-Seidel fast affine projection (GS-FAP) algorithm developed for the linear active noise control (ANC) is further utilized for nonlinear ANC of a 2nd-order Volterra systems with a nonlinear primary path and a noisy secondary path. The simulation results, obtained by applying adaptive Volterra filtering, show that the proposed approach yields more stable and faster nonlinear AN.C, compared with the conventional methods for the nonlinear ANC in case of noisy plant models.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.11
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• pp.1130-1138
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• 2008

A mobile robot should be designed to navigate with collision avoidance capability in the real world, coping with the changing environment flexibly. In this paper, a novel navigation method is proposed for fast moving mobile robots using limit-cycle characteristics of the 2nd-order nonlinear function. It can be applied to robots in dynamically changing environment such as the robot soccer. By adjusting the radius of the motion circle and the direction of the obstacle avoidance, the mobile robot can avoid the collision with obstacles and move to the destination point. To demonstrate the effectiveness and applicability, it is applied to the robot soccer. Simulations and real experiments ascertain the merits of the proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1993.11a
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• pp.90-92
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• 1993

PM3 semiempirical calculations ions have performed on the three aromatic heterocyclic polymers. The dipole moment (${\mu}$7), static 1st-orders(${\alpha}$), 2nd-order (${\beta}$), and 3rd-order polarizabilities(${\gamma}$) were investigated to study the nonlinear optical properties of the polythiophene, polyfuran, and polypyrrole systems. As a function of n, ${\alpha}$ and ${\gamma}$ increases much more rapidly for the polythiophene systems than for the polyfuran and Polypyrrole systems due to the pi-electron delocalization.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.673-677
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• 1986

This paper deals with the state observer-controller which observes unmeasurable state variables of the system and then uses the estimated values as feedback signals. The linearized model is deduced from the nonlinear electrohydraulic servo system. The 4th order analog linear observer-controller and the 2nd order digital one are modelled and implemented using OP amplifiers and IBM PC/XT, respectively. The two observer are experimentally used in the control of an electrohydraulic system. The results are satisfactory in estimation performance and in tracking performance to command signal.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.3 s.43
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• pp.33-42
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• 2005

Frequency domain Volterra model is applied to nonlinear parameter identification procedure for dynamic systems modeled by nonlinear function. The frequency domain Volterra kernels, which correspond io linear, quadratic, and cubic transfer functions in lime domain, are incorporated in nonlinear parametric identification procedure. The nonlinear transfer functions, which can be derived from the Volterra series representation of the nonlinear differential equation of the system by Schetzen's method(1980), are directly used for modeling input output relation. The error is defined by the difference between the observed output and the estimated output which is calculated by substituting the observed input to nonlinear frequency domain model. The system parameters are searched by minimizing the error. Volterra model guarantees enough accuracy and convergence and the estimated coefficients have a good agreement with their actual values not only in the linear frequency region but also in the legion where the $2^{nd}\;or\;3^{rd}$ order nonlinearity is dominant.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.315-318
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• 2003

This paper proposes a switching control method applicable to any affine, 2nd order nonlinear system with single input. The key contribution is to develop a control design method which uses a piecewise continuous Lyapunov function non-increasing at every discontinuous point. The proposed design method requires no restrictions except full state availability. To obtain a non-increasing, piecewise continuous Lyapunov function, we change the sign of off-diagonal term s of the positive definite matrix composing the former Lyapunov function according to the sign of the Inter-connection term. And we use the solution of inequalities which guarantee each Lyapunov function is non-increasing at any discontinuous point.