• 제어로봇시스템학회논문지
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• 제7권12호
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• pp.967-972
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• 2001

In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.177-180
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• 1996

The purpose of this paper is to design a robust adaptive control algorithm for a class of systems having continuous relay nonlinearity. This continuous relay nonlinearity can be defined as an analytic nonlinear function having unknown parameters and bounded unmodeling part. By this mathematical modeling, the whole system can be considered as a nonlinear system having unknown parameters and bounded perturbation. The control algorithm of this paper, RALC, can be constructed by robust adaptive law, feedback linearization, and indirect robust adaptive control. By this RALC, we can obtain that the output of given system can follow that of a stable reference linear model made by designer and the boundedness of all signals in closed-loop system can be maintained. Therefore, we can confirm a robust adaptive control for a class of systems having continuous relay nonlinearity.

• Journal of Electrical Engineering and Technology
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• 제13권5호
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• pp.2125-2133
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• 2018

To achieve high performance speed regulation, a robust adaptive speed controller is proposed for the permanent magnet synchronous motor (PMSM) subject to parameter uncertainties and input saturations in this paper. A nonlinear adaptive control is introduced to compensate the PMSM speed tracking errors due to uncertainties, disturbances and control input saturation constraints. By combining the adaptive control and the nonlinear robust control based on the interconnection and damping assignment (IDA) strategy, a new robust adaptive control is designed for speed regulation of PMSM. Stability and robustness of the closed-loop control system involved with the constrained control inputs rather than unconstrained control inputs are validated. Simulations for PMSM control in the presence of uncertainties and saturations nonlinearities show that the proposed approach is effective to regulate speed, and the average tracking error using the proposed approach is at least 32% smaller than the compared methods.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.19-27
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• 2003

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.292-300
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• 2009

This paper considers a robust adaptive stabilization problem for a class of uncertain nonlinear systems which include an unknown virtual control coefficient, an unknown constant parameter, and a time-varying disturbance whose bound is unknown, We propose a new estimator for an un-known virtual control coefficient and present a robust adaptive backstepping design procedure which results in a smooth state feedback control law, a new two-dimensional parameter update law, and a $C^1$ Lyapunov function which is positive definite and proper.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.34.4-34
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• 2001

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition so called output feedback exponential passivity (OFEP). The designed high gain adaptive controller has simple structure and high robustness with regard to bounded disterbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we deal with a design problem of the robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.327-329
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• 2006

A controller is proposed for the robust adaptive backstepping control of a class of uncertain nonlinear systems using nonlinear disturbance observer (NDO). The NDO is applied to estimate the time-varying lumped disturbance in each step, but a disturbance observer error does not converge to zero since the derivative of lumped disturbance is not zero. Then the fuzzy neural network (FNN) is presented to estimate the disturbance observer error such that the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The proposed control scheme guarantees that all the signals in the closed-loop are semiglobally uniformly ultimately bounded on the basis of the Lyapunov theorem. Simulation results are presented to illustrate the effectiveness and the applicability of the approaches proposed.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권11호
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• pp.510-513
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• 2002

In this paper, the robust adaptive controller design scheme is studied for nonlinear systems in the presence of bounded disturbances A new robust adaptive controller is designed using high-order neural networks, which avoids the singularity problem in adaptive nonlinear control. The stability of the resulting adaptive system with the proposed adaptive controller si guaranteed by suitably choosing the design parameters and initial conditions. I addition, the proposed adaptive controller provides improved transient performance and fast on-line adaptation. The ability and effectiveness of the proposed adaptive control scheme is shown trough simulations of a simple nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.448-453
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• 2004

In this paper, an adaptive robust decentralized excitation control scheme is proposed to enhance the transient stability of a multi-machine power system. We employ a state model where the terminal voltage of each generator is regarded as part of the state. Using this state model, the proposed controller is obtained in two steps: firstly, a robust controller is designed for the nominal system with no interconnection terms; then an adaptive compensator is proposed to deal with those interconnection terms, whose upper bounds are estimated. The resulting adaptive scheme guarantees the practical stability of the closed-loop, and also the uniform ultimate boundedness in the presence of disturbances.

• 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.