• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1505-1506
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• 2008

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control(EFC) using a sliding mode adaptive flux observer and robust speed control(RSC) using a function approximation for mechanical uncertainties. In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer(NDO) using fuzzy neural networks(FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.610-616
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• 2008

This paper presents a new robust sensorless control system for high performance induction motor drives fed by a matrix converter with variable structure. The lumped disturbances such as parameter variation and load disturbance of the system are estimated by a variable structure approach based on model reference adaptive scheme. A Reduced Order Extended Luenberger Observer(ROELO) is also employed to bring better responses at the low speed operation. Experimental results are shown to illustrate the performance of the proposed system.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.117-123
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• 2001

To improve control performance of a non-linear system, many other reserches have used the sliding model control algorithm. The sliding mode controller is known to be robust against nonlinear and unmodeled dynamic terms. However, this algorithm raises the inherent chattering caused by excessive switching inputs around the sliding surface. Therefore, in order to solve the chattering problem and improve control performance, this study has developed the sliding mode controller with a perturbation estimator using the observer-based fuzzy adaptive network. The perturbation estimator based on the fuzzy adaptive network generates the control input of compensating unmodeled dynamics terms and disturbance. And the weighting parameters of the fuzzy adaptive network are updated on-line by adaptive law in order to force the estimation errors converge to zero. Therefore, the combination of sliding mode control and fuzzy adaptive network gives rise to the robust and intelligent routine. For evaluation control performance of the proposed approach, tracking control simulation is carried is carried out for the hydraulic motion simulator which is a 6-degree of freedom parallel manipulator.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.50-58
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• 2009

For precise tracking control of a servo system with nonlinear friction, a robust friction compensation scheme is presented in this paper. The nonlinear friction is difficult to identify the friction parameters exactly through experiments. Friction parameters can be also varied according to contact conditions such as the variation of temperature and lubrication. Thus, in order to overcome these problems and obtain the desired position tracking performance, a robust adaptive back-stepping control scheme with a dual friction observer is developed. In addition, to estimate lumped friction uncertainty due to modeling errors, a DEKF recurrent neural network and adaptive reconstructed error estimator are also developed. The feasibility of the proposed control scheme is verified through the experiment fur a ball-screw system.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.1-14
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• 2004

This paper surveys some existing direct adaptive feedback control schemes for linear time-invariant systems with actuator failures characterized by the failure pattern that some inputs are stuck at some unknown fixed or varying values at unknown time instants, and applications of those schemes to aircraft flight control system models. Controller structures, plant-model matching conditions, and adaptive laws to update controller parameters are investigated for the following cases for continuous-time systems: state tracking using state feed-back, output tracking using state feedback, and output tracking using output feedback. In addition, a discrete-time output tracking design using output feedback is presented. Robustness of this design with respect to unmodeled dynamics and disturbances is addressed using a modified robust adaptive law.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.5-8
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• 1996

This paper proposea a new adaptive algorithm to improve the performance fo a robust adaptive control system. This adaptive algorithm counteracts the effects of disturbances and makes the time derivative of Lyapunov function non-positive, $V{\leq}0$. As a result, the output error approaches zero as $t\;\rightarrow\;\infty$ not only in the presence of bounded disturbances, but also in the ideal case. The effectiveness of this proposed algorithm is demonstrated by the stability analysis and simulation results.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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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.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.322-325
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• 1994

Mareel and Bitmead proved the presence of chaotic signal in random noise by applying dead beat control theory to adaptive mechanism. In this paper robust adaptive theory is proposed. With the property of chaotic signal that has order and law, the proposed theory can enhance the control Performance by applying the recursive algorithm that uses dynamic relation which have small correlation. The performance of proposed algorithm is demonstrated with the computer simulation of position control of electric motor. In this simulation, the adaptive low is adopted to control electric motor and the Presence of chaotic signal in feedback signal is proved by using several method such as time series, fourier spectrum phase portrait method.

• Nuclear Engineering and Technology
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• v.34 no.6
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• pp.553-565
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• 2002

In this paper, the robust control scheme with the improved control performance within the boundary layer is proposed. In the control scheme, the robust controller based on the traditional variable structure control method is modified to have the adaptation within the boundary layer. From this controller, the width of the boundary layer where the robust control input is smoothened out can be given by an appropriate value. But the improved control performance within the boundary layer can be achieved without the so-called control chattering because the role of adaptive control is to compensate for the uncovered portions of the robust control occurred from the continuous approximation within the boundary layer Simulation tests for circular navigation of an underwater wall-ranging robot developed for inspection of wall surfaces in the research reactor, TRIGA MARK III, confirm the performance improvement. Notational Conventions Vectors are written in boldface roman lower-case letters, e.g., x and y. Matrices are written in upper-case roman letters, e.g., G and B. And ∥.∥ means the Euclidean norm.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.385-394
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• 2004

This paper proposes the generalized structure of a model-based disturbance rejection controller called a Robust Internal-loop Compensator (RIC). The framework consists of the RIC in the internal-loop to eliminate disturbances and a feedback controller in the external-loop to achieve nominal control performance. As the main contribution of this paper, we redefine the design problem of the RIC as a regulation control problem, then show that this new definition with the RIC structure provides more design flexibility and less implementation constraints. This is verified through a comparative structural analysis with Disturbance Observer (DOB) and Adaptive Robust Control (ARC). Two design examples of the RIC are given, along with practical issues that should be considered in the design procedure. The proposed framework is demonstrated by simulations of a rotary-type motor and experiments with a linear-type motor system.