• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권11호
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• pp.503-509
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• 2002

In this paper, the robust non-fragile guaranteed cost control problem is studied for a class of linear large-scale systems with uncertainties and a given quadratic cost functions. The uncertainty in the system is assumed to be norm-bounded and time-varying. Also, the state-feedback gains for subsystems of the large-scale system are assumed to have norm-bounded controller gain variations. The problem is to design a state feedback control laws such that the closed-loop system is asymptotically stable and the closed-loop cost function value is not more than a specified upper bound for all admissible uncertainties and controller gain variations. Sufficient conditions for the existence of such controllers are derived based on the linear matrix inequality (LMI) approach combined with the Lyapunov method. A parameterized characterization of the robust non-fragile guaranteed cost controllers is given in terms of the feasible solutions to a certain LMI. A numerical example is given to illustrate the proposed method.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.516-523
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• 2013

This paper investigates an improved stator flux linkage observer for sensorless permanent magnet synchronous motor (PMSM) drives using a voltage-based flux linkage model and an adaptive sliding mode variable structure. We propose a new observer design that employs an improved sliding mode reaching law to achieve better estimation accuracy. The design includes two models and two adaptive estimating laws, and we illustrate that the design is stable using the Popov hyper-stability theory. Simulation and experimental results demonstrate that the proposed estimator accurately calculates the speed, the stator flux linkage, and the resistance while overcoming the shortcomings of traditional estimators.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권2호
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• pp.145-150
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 정기총회 및 창립40주년기념 학술대회 학회본부
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• pp.69-72
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• 1987

General schemes for the adaptive control and identification of multivariable systems by model reference approach are developed. Lyapunov's direct method and LaSalle's theorem are employed to ensure the stability of these schemes. An added feature is the simplicity of the stable adaptive laws, which depend explicitly on the state variables of plant and model, and on the plant input. Computer simulation results of several examples illustrate the the effectiveness of the proposed schemes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2424-2426
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• 2002

This paper proposes an adaptive control method of partially known system and shows its application result to control for two-wheeled WMR. The controlled system is stable in the sense of Lyapunov stability. To design a tracking controller for welding path reference, an error configuration is defined and the controller is designed to drive the error to zero as fast as desired. Moments of inertia of system are considered to be unknown system parameters. Their values are estimated using update laws in adaptive control scheme. The effectiveness of the proposed controller is shown through simulation results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.

• 전기학회논문지P
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• 제61권4호
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• pp.161-167
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• 2012

Energy conversion systems such as power inverters and converters are basically significant in establishing photovoltaic power systems to enhance power effectiveness. This paper proposes a new converter control method by using the Lyapunov redesign approach. We construct the proposed control mechanism linearly composed of nominal control and auxiliary control laws. The former is generally designed through a well-known power electronic technology and the latter is implemented to compensate real-time control error due to uncertain natures of converter systems in practice. For realizing adaptive control capability in the proposed control mechanism, a control parameter vector is estimated by utilizing a steepest descent based optimization method. We carry out numerical simulation with Matlab(c) software to demonstrate reliability of the proposed converter control system and conduct a comparative study to prove its superiority by comparing with a generic converter control methodology.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2308-2310
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• 2000

In adaptive fuzzy control, fuzzy systems are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure. This paper considers general nonlinear systems and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters and fuzzy rule structrue are established so that the whole system is stable in the sense of Lyapunov.