• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.501-501
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• 2000

In this paper, the theoretical development to stabilize a class of uncertain time-delay systems via sliding mode control is presented. The system under consideration is described in state space model containing state delay, uncertain parameters and disturbance. The main idea is to reduce the state of delayed system, by employing the generalize linear transformation, into an equivalent one with no delay inside, which is easier to analyze its behavior and stability. Then, the sliding control approach is employed to find the stabilizing control law. Finally, a numerical simulation is illustrated to show the algorithm for applying the proposed theorems and the efffetiveness of the designed control law in stabilizing the controlled systems.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.5
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• pp.117-125
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• 1995

An improved robust control law is proposed for uncertain rigid robots. The uncertainty is nonlinear and (possibly fast) time-varying. Therefore, the uncertain factors such as imperfect modeling, friction, payload change, and external disturbances are all addressed. Based on the possible bound of the uncertainty, the controller is constructed. For uncertain flexible-joint robots, some feedback control terms are then added to the proposed robust control law in order to stabilize the elastic vibrations at the joints. To show that the proposed control laws are indeed applicable, the stability study based on Lyapunov function, a singular perturbation approach, and simulation results are presented.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.620-623
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• 1997

This paper is concerned with the design of a suboptimal Kalman filter with robust state estimation performance for system models represented in the state space, which are subjected to parameter uncertainties in both the state and measurement matrices. Under the assumption that the uncertain system is quadratically stable, if the augmented system composed of the uncertain system and the filter is controllable, the proposed filter can provide the upper bound of the estimation error variance for all admissible uncertain parameters. This upper bound can be represented as the convex function of a parameter introduced in the design procedure, and the optimized upper bound of the estimation error variance can also be found via the optimization of this convex function.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.631-634
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• 2014

We propose a measurement state feedback controller for a class of nonlinear systems that have uncertain nonlinearity and sensor noise. The new design method based on the matrix inequality approach solves the measurement feedback control problem of a class of nonlinear systems. As a result, the proposed methods using a matrix inequality approach has the flexibility to apply the controller. In addition, the sensor noise can be attenuated for more generalized systems containing uncertain nonlinearities.

• International Journal of Aerospace System Engineering
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• v.6 no.2
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• pp.1-10
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• 2019

In this paper, the probabilistic free vibration analysis of a geometrically coupled cantilever wing with uncertain material properties is carried out using stochastic finite element (SFEM) based on first order perturbation technique. Here, both stiffness and damping of the system are considered as random parameters. The bending and torsional rigidities are assumed as spatially varying second order Gaussian random fields and represented by Karhunen Loeve (K-L) expansion. Here, the expected value, standard deviation, and probability distribution of random natural frequencies and damping ratios are computed. The results obtained from the present approach are also compared with Monte Carlo simulations (MCS). The results show that the uncertain bending rigidity has more influence on the damping ratio and frequency of modes 1 and 3 while uncertain torsional rigidity has more influence on the damping ratio and frequency of modes 2 and 3.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.11
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• pp.1612-1619
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• 2017

In this note, a complete proof of Utkin's theorem is presented for SI(single input) uncertain nonlinear systems. The invariance theorem with respect to the two nonlinear transformation methods so called the two diagonalization methods is proved clearly, comparatively, and completely for SI uncertain nonlinear systems. With respect to the sliding surface and control input transformations, the equation of the sliding mode i.e., the sliding surface is invariant, which is proved completely. Through an illustrative example and simulation study, the usefulness of the main results is verified. By means of the two nonlinear transformation methods, the same results can be obtained.

• Management Science and Financial Engineering
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• v.21 no.1
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• pp.1-9
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• 2015

We consider an m-machine flow shop scheduling problem to minimize the latest completion time, where processing times are uncertain. Processing time uncertainty is described through a finite set of processing time vectors. The objective is to minimize maximum deviation from optimality for all scenarios. Since this problem is known to be NP-hard, we consider it with an ordered property. We discuss optimality properties and develop a pseudo-polynomial time approach for the problem with a fixed number of machines and scenarios. Furthermore, we find two special structures for processing time uncertainty that keep the problem NP-hard, even for two machines and two scenarios. Finally, we investigate a special structure for uncertain processing times that makes the problem polynomially solvable.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.149-152
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• 2005

In this paper, a robust $H\infty$ stabilization problem to a uncertain discrete-time fuzzy systems with time-varying delay via static output feedback is investigated. The Takagi -Sugeno (T-S) fuzzy model is employed to represent an uncertain nonlinear systems with time-varying delayed state. Using a single Lyapunov function, the globally asymptotic stability and disturbance attenuation of the closed-loop fuzzy control system are discussed. Sufficient conditions for the existence of robust $H\infty$ controllers are given in terms of linear matrix inequalities.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.