• Journal of Electrical Engineering and Technology
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• 제11권4호
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• pp.1012-1019
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• 2016

A robust control intended for a nonholonomic mobile robot is considered to guarantee good tracking a desired trajectory. The main drawbacks of the mobile robot model are the existence of nonholonomic constraints, uncertain system parameters and un-modeled dynamics. in order to overcome these drawbacks, we propose a robust control based on Lyapunov theory associated with sliding-mode control, this solution shows good robustness with respect to parameter variations, measurement errors, noise and guarantees position and velocity tracking. The global asymptotic stability of the overall system is proven theoretically. The simulation results largely confirm the effectiveness of the proposed control.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.54-67
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• 2003

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.234-236
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• 1995

A robust nonlinear speed control of a permanent magnet synchronous motor(PMSM) is presented. A perturbed dynamic model including the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is proposed and compared with the conventional controller.

• Earthquakes and Structures
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• 제10권6호
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• pp.1253-1272
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• 2016

For a nonlinear control system, there are many uncertainties, such as the structural model, controlled parameters and external loads. Although the significant progress has been achieved on the robust control of nonlinear systems through some researches on this issue, there are still some limitations, for instance, the complicated solving process, weak conservatism of system, involuted structures and high order of controllers. In this study, the computational structural mechanics and optimal control theory are adopted to address above problems. The induced norm is the eigenvalue problem in structural mechanics, i.e., the elastic stable Euler critical force or eigenfrequency of structural system. The segment mixed energy is introduced with a precise integration and an extended Wittrick-Williams (W-W) induced norm calculation method. This is then incorporated in the market-based control (MBC) theory and combined with the force analogy method (FAM) to solve the MBC robust strategy (R-MBC) of nonlinear systems. Finally, a single-degree-of-freedom (SDOF) system and a 9-stories steel frame structure are analyzed. The results are compared with those calculated by the $H{\infty}$-robust (R-$H{\infty}$) algorithm, and show the induced norm leads to the infinite control output as soon as it reaches the critical value. The R-MBC strategy has a better control effect than the R-$H{\infty}$ algorithm and has the advantage of strong strain capacity and short online computation time. Thus, it can be applied to large complex structures.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.523-525
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• 1998

In this paper, a robust control scheme for a class of SISO nonlinear dynamical system is proposed by using output-feedback linearization method. The presented control scheme is based on the VSS control theory concept. In this control scheme, we assume that the nonlinear dynamical system is minimum phase, i.e., the relative degree of the system is r < n and zero dynamics is stable. We also assume that the states of zero dynamics are not accessible. It is shown that the global asymptotically stability is guaranted under the proposed control scheme. The feasibility of the proposed control scheme is verified through a computer simulation.

• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.128-137
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• 2007

A robust adaptive controller design for a class of Markovian jump parametric -strict-feedback systems is given. The disturbances considered herein include both uncertain nonlinearities and Wiener noises of unknown covariance. And they satisfy some bound-conditions. By using stochastic Lyapunov method in Markovian jump systems, a switching robust adaptive controller was obtained that guarantees global uniform ultimate boundedness of the closed-loop jump system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.703-705
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• 2000

In this paper, we design a robust adaptive controller for a nonlinear systems with uncertainties to be rejected via disturbance adaptation law. The nonlinear system considered in this paper has unknown nonlinear functions being influenced by external disturbance. The upper bounds of unknown nonlinear functions at each time is estimated by using disturbance adaptation law. The estimated nonlinear functions are used to design stabilizing function and control of input. Tuning function is used to estimate unknown system parameter without overparametrization. A set-point regulation error converges to a residual set close to zero asymptotically fast.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.583-595
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• 2008

In this paper, we introduce a fuzzy nonlinear feedback approach to the control of a class of chaotic dynamical systems. The fuzzy Parallel Distributed Compensation with Reduced Rule Base approach (PDC_RRB) is proposed. The design procedure is conceptually simple and considered to a nonlinear optimal and robust control problem due to the nonlinear nature of the Takagi-Sugeno (TS) fuzzy system. Simulation results are provided to show the effictiveness of the proposed methodology.

• 제어로봇시스템학회논문지
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• 제10권4호
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• pp.312-316
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• 2004

In this paper, it is presented an adaptive robust control system to implement real-time control of a robot manipulator. There are Quantitative or qualitative differences between a real robot manipulator and a robot modeling. In order to compensate these differences, uncertain factors are added to a robot modeling. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, etc. Also, uncertainty is often nonlinear and time-varying. In the proceeding work, we proposed a class of robust control of a robot manipulator and provided the stability analysis. In the work, we propose a class of adaptive robust control of robot manipulator with bound estimation. Through experiments, the proposed adaptive robust control scheme is proved to be an efficient control technique for real-time control of a robot system using DSP.