• Journal of Institute of Control, Robotics and Systems
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• v.6 no.7
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• pp.523-528
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• 2000

In this short note the characteristics of a nonlinear system of which the state trajectories are oscillating in the phase plane are overviewed. The physical concept of stuck and sliding patch phenomena are also introduced by adding some switching functions and their stability on the sliding patches are analyzed by using the Lyapunov stability theory and Frobenius theorem.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.12
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• pp.1269-1272
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• 1991

New Sufficient conditions for linear time varying systems to be asymptotically stable are presented by using the Lyapunov function approach. One is the generalized version of the previous result, and the other is obtained using the Lyapunov function theorem and matrix properties. Also we compare the presented results with the previous results with the previous results and provide examples to show the usefulness of our results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1689-1696
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• 2000

This paper proposes a new adaptive control scheme for flexible-link robots. A model-based nonlinear control scheme is designed based on a V-shape Lyapunov function, and then the nonlinear control i s extended to a model-based adaptive control to cope with parametric uncertainties in the dynamic model. The proposed control guarantees the global exponential or global asymptotic stability of the overall control system with all internal signals bounded. The effectiveness of the proposed control is shown by computer simulation.

• Journal of IKEEE
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• v.17 no.1
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• pp.83-88
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• 2013

This paper presents the output feedback control design for feedforward nonlinear systems with input and output delay. The proposed output feedback controller is composed of a linear observer and a linear controller. It is shown that by using Lyapunov-Krasovskii theorem, the proposed controller ensures a global asymptotic stability for arbitrarily large delay. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.203-207
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• 1991

In this paper, a linear stocastic dynamic system with norm - bounded plant perpurbations and norm - bounded noise covariarice is studied. Instead of Bellman-Gronwall inequality used in previous study, Lyapunov stability theorem is used to derive stability condition. The new condition is of more compact form than the previous result.

• 전기의세계
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• v.49 no.9
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• pp.4-8
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• 2000

Recently we have shown based on Lyapunov theorem that the closed loop system with the constrained infinite horizon H$\infty$ optimal controller is exponentially stable. moreover the on-line feedback implementation of the constrained infinite horizon H$\infty$ optimal control based on quadratic programs has been proposed. n this paper we summarize and discuss these results.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1438-1454
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• 2016

Because the nonlinear and time-varying characteristics of continuously variable transmission (CVT) systems driven by means of a six-phase copper rotor induction motor (CRIM) are unconscious, the control performance obtained for classical linear controllers is disappointing, when compared to more complex, nonlinear control methods. A blend modified recurrent Gegenbauer orthogonal polynomial neural network (OPNN) control system which has the online learning capability to come back to a nonlinear time-varying system, was complied to overcome difficulty in the design of a linear controller for six-phase CRIM driving CVT systems with lumped nonlinear load disturbances. The blend modified recurrent Gegenbauer OPNN control system can carry out examiner control, modified recurrent Gegenbauer OPNN control, and reimbursed control. Additionally, the adaptation law of the online parameters in the modified recurrent Gegenbauer OPNN is established on the Lyapunov stability theorem. The use of an amended artificial bee colony (ABC) optimization technique brought about two optimal learning rates for the parameters, which helped reform convergence. Finally, a comparison of the experimental results of the present study with those of previous studies demonstrates the high control performance of the proposed control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.463-467
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• 2005

In this paper, the guidance law applicable to formation flight of UAV in three-dimensional space is proposed. The concept of miss distance, which is commonly used in the missile guidance laws, and Lyapunov stability theorem are effectively combined to obtain the guidance commands of the wingmen. The propose guidance law is easily integrated into the existing flight control system because the guidance commands are given in terms of velocity, flight path angle and heading angle to form the prescribed formation. In this guidance law, communication is required between the leader and the wingmen to achieve autonomous formation. The wingmen are only required the current position and velocity information of the leader vehicle. The performance of the proposed guidance law is evaluated using the complete nonlinear 6-DOF aircraft system. This system is integrated with nonlinear aerodynamic and engine characteristics, actuator servo limitations for control surfaces, various stability and control augmentation system, and autopilots. From the nonlinear simulation results, the new guidance law for formation flight shows that the vehicles involved in formation flight are perfectly formed the prescribed formation satisfying the several constraints such as final velocity, flight path angle, and heading angle.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.408-421
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• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.