• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.9-16
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• 2004

In this paper, we propose a robust controller for robot manipulators with parametric uncertainties using feature-based visual servo control system. In order to improve trajectory error of the robot manipulators due to the parameter variation, integral action is included in the dynamic control of part in inner subroutine of the control system. This integral action also reduces feature error in the steady state. The stability analysis of the closed-loop system is shown by the Lyapunov method. The effectiveness of the proposed method is shown by simulation and experimental results on the 5 link robot manipulator with two degree of freedom.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.21-31
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• 2000

In this paper, an analysis of the stability for a class of discrete-time neural network control systems is presentd. Based on Lyapunov's direct method, a sufficient stability condition for the neural network control systems is systematically derived and the modified back propagation algorithm which reflects the derived stability condition is suggested. The modified BP originates from the derived sufficient condition and guarantees the exponential stability of the resulting trained closed system. Finally, computer simulation is included to show an example where the derived stability condition and the BP modified bythe condition is used to train the control plant.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.523-530
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• 2007

Crane systems are very important in industrial fields to carry heavy objects such that many investigations about control of the systems are actively conducted for enhancing its control performance. This paper presents an adaptive control approach using the model matching for a complex 3-DOF nonlinear crane system. First, the system model is linearized through feedback linearization method and then PD control is applied in the approximated model. This linear model is considered as nominal to derive corrective control law for a perturbed crane model using Lyapunov theory. This corrective control is primitively aimed to compensate real-time control deviation due to partially known perturbation. We additionally study stability analysis of the crane control system using Lyapunov perturbation theory. Evaluation of our control approach is numerically carried out through computer simulation and its superiority is demonstrated comparing with the classical control.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.526-532
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• 2014

In this paper, we present and analyze a LQ (Linear Quadratic) inverse optimal state-consensus protocol for continuous-time multi-agent systems with undirected graph topology. By Lyapunov analysis of the state-consensus error dynamics, we show the sufficient conditions on the algebraic connectivity of the graph to guarantee LQ inverse optimality and closed-loop stability. A more relaxed stability condition is also provided in terms of the algebraic connectivity. Finally, a formation control protocol for multiple mobile robots is proposed based on the target LQ inverse optimal consensus protocol, and the simulation results are provided to verify the performance of the proposed LQ inverse formation control method.

• Bulletin of the Korean Mathematical Society
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• v.54 no.6
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• pp.1893-1912
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• 2017

In this paper, we aim to construct a nonstandard finite difference (NSFD) scheme to solve numerically a mathematical model for cholera epidemic dynamics. We first show that if the basic reproduction number is less than unity, the disease-free equilibrium (DFE) is locally asymptotically stable. Moreover, we mainly establish the global stability analysis of the DFE and endemic equilibrium by using suitable Lyapunov functionals regardless of the time step size. Finally, numerical simulations with different time step sizes and initial conditions are carried out and comparisons are made with other well-known methods to illustrate the main theoretical results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.231-236
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• 2002

In this paper, we present an analysis and design method fur the control of input-saturated nonlinear systems with the time-delay. The target system is represented by Takagi-Sugeno (T-S) fuzzy model and the parallel distributed compensation (PDC) controller is designed to guarantee the local stability of the equilibrium point. We derive the sufficient condition for the local stability by applying Lyapunov-krasovskii theorem and this condition is converted into the LMI problem.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.401-406
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• 2013

The mobile robot is one of the widely-used systems in service industry. We propose a gain-scheduling feedback controller for the tracking control of the mobile robot. The benefit of our proposed controller is that it avoids the singularity issue occurs with the controllers suggested in [4], [10]. Moreover, we show the stability analysis of the controlled system via a Lyapunov stability approach such that the exponential convergence of tracking error to zero is analytically provided. The simulation results show the validity of the proposed controller and improved control performance over the conventional controller.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.251-260
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• 2015

The main purpose of this paper is to describe a DTC (direct torque control) method for four-switch brushless dc motor (BLDCM) drive. In the method, a novel voltage space vector modulation scheme, an optimal switching table, and a flux observation method are proposed. Eight voltage vectors are summarized, which are selected to control BLDCM in SVPWM pattern, and an optimal switching table is proposed to improve the torque distortion caused by midpoint current of the split capacitors. Unlike conventional flux observers, this observer does not require speed adaptation and is not susceptible to speed estimation errors, especially, at low speed. Global asymptotic stability of the flux observer is guaranteed by the Lyapunov stability analysis. DC-offset effects are mitigated by introducing a PI component in the observer gains. This method alleviates the undesired current and torque distortion which is caused by uncontrollable phase. The correctness and feasibility of the method are proved by simulation and experimental results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.407-414
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• 2000

This paper addresses the analysis and design of fuzzy control systems for a class of complex uncertain single-input single-output nonlinear systems. The proposed method represents the nonlinear system using a Takagi-Cugeno fuzzy model and construct a global fuzzy logic controller by blending all local state feedback controllers with a sliding mode controller. Unlike the commonly used parallel distributed compensation technique, we can design a global stable fuzzy controller without finding a common Lyapunov function for all local control systems, and can obtain good tracking performance by using sliding mode control theory. Furthermore, stability analysis is carried out not for the fuzzy model but for the real nonlinear system with uncertainties. Duffing forced oscillation sysmte is used as an example to show the effectiveness and feasibility of the proposed method.