• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.589-592
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• 1996

It is well known that robust compensators designed by the block-diagonal Lyapunov function approaches are conservative while they are popular in practice because of their computational easiness. In this note, we develop a systematized version of conventional block-diagonal Lyapunov function approaches by deriving two separated optimizations based on the guaranteed cost control method. The proposed method generates reasonable robust compensators in practice.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.2
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• pp.97-102
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• 2009

In this paper, the problem of stability analysis for networked control systems with norm-bounded parameter uncertainties is investigated. By construction Lyapunov's functional, a new delay-dependent stability criterion for uncertain networked control system is established in terms of LMIs (linear matrix inequalities) which can be easily by various convex optimization algorithms. One numerical example is included to show the effectiveness of proposed criterion.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.133-134
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• 2007

A swing-up control strategy is suggested for a rotary inverted pendulum with restricted rotation range. In order to take the rotation range limitation into account, a new Lyapunov function used for energy-based control is proposed a control strategy is derived from the Lyapunov function. Futhermore, optimization-base parameter estimation is adopted to get an exact mathematical model for the pendulum. Simulation results show that the proposed control strategy swings up the rotary inverted pendulum efficiently.

• KIEE International Transaction on Systems and Control
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• v.12D no.1
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• pp.39-42
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• 2002

This paper focuses on the asymptotic stability of a class of neutral linear systems with mixed time-varying delay arguments. Using the Lyapunov method, a delay-dependent stability criterion to guarantee the asymptotic stability for the systems is derived in terms of linear matrix inequalities (LMIs). The LMIs can be easily solved by various convex optimization algorithms. Two numerical examples are given to illustrate the proposed methods.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.1
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• pp.67-71
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• 2009

This paper presents a rank-constrained linear matrix inequality (LMI) approach to the design of a multi-objective controller such as $H_2/H_{\infty}$ control. Multi-objective control is formulated as an LMI optimization problem with a nonconvex rank condition, which is imposed on the controller gain matirx not Lyapunov matrices. With this rank-constrained formulation, we can expect to reduce conservatism because we can use separate Lyapunov matrices for different control objectives. An iterative penalty method is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1587-1605
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• 2015

Mobile cloud computing (MCC) combines mobile Internet and cloud computing to improve the performance of applications. In MCC, the data processing and storage for mobile devices (MDs) is provided on the remote cloud. However, MCC faces the problem of energy efficiency caused by randomly varying channels. In this paper, by introducing the Lyapunov optimization method, we propose a combined service subscription and delivery (CSSD) algorithm that can guide the users to subscribe to services reasonably. This algorithm can also determine whether to deliver the data and to whom data is sent in the current time unit based on the queue backlog and the channel state. Numerical results validate the correctness and effectiveness of our proposed CSSD algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.685-689
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• 2005

In this paper, we consider a robust output feedback model predictive controller(MPC) design for Wiener model. Nonlinearities that couldn't be represented in static nonlinearity block of Wiener model are regarded as uncertainties in linear block. An dynamic output feedback controller design method is presented for Wiener MPC. According to MPC algorithm, the control law is computed based on linear matrix inequality(LMI)at each sampling time by solving convex optimization. Also, a new parameter dependent Lyapunov function is proposed to get a less conservative condition. The results are illustrated with numerical example.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.11
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• pp.2119-2130
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• 2011

This paper proposes new delay-dependent robust stability criteria for neural networks with time-varying delays. By construction of a suitable Lyapunov-Krasovskii's (L-K) functional and use of Finsler's lemma, new stability criteria for the networks are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.