• Journal of Institute of Control, Robotics and Systems
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• v.5 no.2
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2497-2499
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• 2002

This raper proposes a simple robust nonlinear controller design method based on Lyapunov stability for tracking reference welding trajectory and velocity of a three wheel welding mobile robot (WMR). Control law is obtained from Lyapunov control function to ensure asymptotical stability of the system. The effectiveness of the proposed controller is shown through simulation results.

• Proceedings of the KIPE Conference
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• 1998.11a
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• pp.23-27
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• 1998

This paper presents a new speed and position sensorless control method of permanent magnet synchronous motors based on the sliding mode observer. The sliding mode observer structure and its design method are discussed. Also, Lyapunov functions ar chosen for determining the adaptive law for the speed and the stator resistance estimator. The effectiveness of the proposed observer is confirmed by the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.54.1-54
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• 2001

In this paper, an alternate method for state-covariance assignment for SISO(single input singe output) linear systems is proposed. This method is based on the inverse solution of the Lyapunov matrix equation and the resulting formulas are similar in structure to the formulas for pole placement. Further, the set of all assignable covariance matrices to a SISO linear system is also characterized.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.3
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• pp.266-272
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• 2004

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method will be used to estimate the vehicle parameters such as mass, time constant, etc. The nonlinear model between the driving force and the vehicle acceleration will be chosen to design the state observer for the vehicle velocity and acceleration. It will be shown that the proposed observer is exponentially stable, and that the adaptive controller proposed in this paper is stable by the Lyapunov function candidate. It will be proved that the errors of the relative distance, velocity and acceleration converge to zero asymptotically fast, and that the overall system is also asymptotically stable. The simulation results are presented to investigate the effectiveness of the proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.1
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• pp.33-44
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• 1989

A controller disign method is proposed for multivariable nonlinear stochastic systems with hard nonlinearities such as Coulomb friction, backlash and saturation. In order to take the nonlinearities into account statistical linearization techniques are used. And multi- variable pole placement techniques are applied to design controller for the statistically linearized multivariable systems. The basic concept of the controller design method is to solve two coupled equations, characteristic equation and Lyapunov equation, simultaneously and iteratively for statistically linearized multivariable stochastic systems. An aircraft with saturation serves as a design example. The design example illustrates the influence of nonlinear effects. The results of the analysis are compared to Monte Carlo simulation to test their accuracy.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.9-19
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• 2013

This paper deals with fuzzy $H_{\infty}$ filtering problem of discrete-time nonlinear Markovian jump systems with state and output time delays. The purpose is to design fuzzy $H_{\infty}$ filter such that the corresponding estimation error system with time delays and initial state uncertainties is stochastically stable and satisfies an $H_{\infty}$ performance level. A sufficient condition for the existence of fuzzy $H_{\infty}$ filter is given in terms of matrix inequalities. In order to relax conservatism, a stochastic mode dependent fuzzy Lyapunov function is employed. The Lyapunov function not only is dependent on the operation modes of system, but also includes the fuzzy membership functions. An illustrative example is finally given to show the applicability and effectiveness of the proposed method.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.549-556
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• 2020

This paper proposes several suboptimal methods of designing a controller for a networked control system with state feedback where delay due to transmission error and transmission delay is modeled as a Markov process. A stability condition for a control system with Markov delay is found through an equivalent relationship that corresponding delay-dependent Lyapunov-Krasovskii functional has the same form of the Lyapunov function of an augmented control system. Several suboptimal methods of designing a controller from the stability condition are proposed to reduce complexity. A simple numerical experiment shows that a restricted subspace method which limits the search space of a matrix variable to a block diagonal form provides the best tradeoff between the complexity and performance.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.110-112
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• 2000

A new robust digital fuzzy controller design methodology is proposed for load frequency of nonlinear power system with valve position limits of governor in the presence of parametric uncertainties. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the nonlinear power system. A sufficient condition of robust stability for robust fuzzy control with parametric uncertainties is presented in the sense of Lyapunov. The controller that designed by preposed robust fuzzy controller design method is based compounding condition between continues system and discrete system. The effectiveness of controller that designed by the proposed robust fuzzy controller design method is demonstrated through simulation example.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.1-14
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• 2008

This paper presents a convex optimization method for observer-based mixed $H_2/H_{\infty}$ control design of linear systems with time-varying state, input and output delays. Delay-dependent sufficient conditions for the design of a desired observer-based control are given in terms of linear matrix inequalities (LMIs). An observer-based controller which guarantees asymptotic stability and a mixed $H_2/H_{\infty}$ performance for the closed-loop system of the linear system with time-varying delays is then developed. A Lyapunov-Krasovskii method underlies the observer-based mixed $H_2/H_{\infty}$ control design. A numerical example with simulation results illustrates the effectiveness of the methodology.