• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.61-64
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• 1986

The Fourier Spectral Analysis has been widely utilized in the analysis of linear dynamic systems. However, it may not be generaly extended to analyze nonlinear systems. In this paper, a linear underlying dynamic structure coupled with nonlinear elements is analyzed by using newly derived equations of motion after the linear dynamic structure is characterized by the Fourier spectral analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.284-284
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• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.368-373
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• 1998

This paper presents a GA-based fuzzy modelling scheme of nonlinear systems. The fuzzy model is a type of the Sugeno-Tagaki's fuzzy model whose consequence parts are described by a linear continuous dynamic equation as subsystem of a nonlinear system. The centers and width of the membership functions of the fuzzy sets defined over the input space and the orders and parameters of subsystems in the consequence parts are adjusted by a genetic algorithm. The effectiveness of the proposed method is verified

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.7
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• pp.795-805
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• 2016

Nonlinear normal modes(NNMs) is a branch of periodic solution of nonlinear dynamic systems. Determination of stable periodic solution is very important in many engineering applications since the stable periodic solution can be an attractor of such nonlinear systems. Periodic solutions of nonlinear system are usually calculated by perturbation methods and numerical methods. In this study, numerical method is used in order to calculate the NNMs. Iteration of the solution is presented by multiple shooting method and continuation of solution is presented by pseudo-arclength continuation method. The stability of the NNMs is analyzed using Floquet multipliers, and bifurcation points are calculated using indirect method. Proposed analyses are applied to two nonlinear numerical models. In the first numerical model nonlinear spring-mass system is analyzed. In the second numerical model Jeffcott rotor system which has unstable equilibria is analyzed. Numerical simulation results show that the multiple shooting method can be applied to self excited system as well as the typical nonlinear system with stable equilibria.

• ETRI Journal
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• v.32 no.4
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• pp.588-595
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• 2010

This paper proposes encoding and decoding for nonlinear product codes and investigates the performance of nonlinear product codes. The proposed nonlinear product codes are constructed as N-dimensional product codes where the constituent codes are nonlinear binary codes derived from the linear codes over higher order alphabets, for example, Preparata or Kerdock codes. The performance and the complexity of the proposed construction are evaluated using the well-known nonlinear Nordstrom-Robinson code, which is presented in the generalized array code format with a low complexity trellis. The proposed construction shows the additional coding gain, reduced error floor, and lower implementation complexity. The (64, 24, 12) nonlinear binary product code has an effective gain of about 2.5 dB and 1 dB gain at a BER of $10^{-6}$ when compared to the (64, 15, 16) linear product code and the (64, 24, 10) linear product code, respectively. The (256, 64, 36) nonlinear binary product code composed of two Nordstrom-Robinson codes has an effective gain of about 0.7 dB at a BER of $10^{-5}$ when compared to the (256, 64, 25) linear product code composed of two (16, 8, 5) quasi-cyclic codes.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.150-154
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• 1993

A new method is proposed for obtaining Volterra kernals of a nonlinear system by use of a nonlinear systems by use of pseudorandom M-sequences and correlation technique. M-sequence is applied to a nonlinear technique. M-sequence is applied to a nonlinear system and the crosscorrelation function between the input and the output displays not only the linear impulse response of the linear part of the system, but also crosssections of the Volterra kernals of nonlinear system. Simulations are carried out for up to 3rd order Volterra kernal, and the results show a good agreement with the theoretical considerations.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1197-1200
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• 2010

The problem of designing a nonlinear sliding surface for an uncertain system is considered. The proposed sliding surface comprises a linear time invariant term and an additional time varying nonlinear term. It is assumed that a linear sliding surface parameter matrix guaranteeing the asymptotic stability of the sliding mode dynamics is given. The linear sliding surface parameter matrix is used for the linear term of the proposed sliding surface. The additional nonlinear term is designed so that a Lyapunov function decreases more rapidly. By including the additional nonlinear term to the linear sliding surface parameter matrix we obtain a nonlinear sliding surface such that the speed of responses is improved. We also give a switching feedback control law inducing a stable sliding motion in finite time. Finally, we give an LMI-based design algorithm, together with a design example.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.137-140
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• 1999

A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.7
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• pp.605-613
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• 2001

In this paper, we design a robust adaptive controller for a nonlinear system with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered has unknown nonlinear functions being influenced by external disturbance. The upper bound of unknown nonlinear functions at each time is estimated by using a disturbance adaptation law. The estimated nonlinear functions are used to design a stabilizing function a control input. Tuning function is used to estimates unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically. The effectiveness of the proposed controller is investigated by computer simulation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.2
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• pp.76-82
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• 2009

A Heating, Ventilating and Air Conditioning (HVAC) system is a nonlinear multi-input multi-output (MIMO) system. This system is very difficult to control the temperature and the humidity ratio of a thermal space because of complex nonlinear characteristics. This paper proposes an adaptive fuzzy output feedback control based on observer for the nonlinear HVAC system. The nonlinear HVAC system is linearized through dynamic extension. State observers are designed for estimating state variables of the HVAC system. Fuzzy systems are employed to approximate uncertain nonlinear functions of the HVAC system with unavailable state variables. The obtained controller compares with an adaptive feedback controller. Simulation is given to demonstrate the effectiveness of our proposed adaptive fuzzy method.