• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.925-926
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• 2006

This paper presents a sensorless speed control of an interior permanent magnet synchronous motor using an adaptive integral binary observer and fuzzy logic controller. In view of composition with a main loop regulator and an auxiliary loop regulator, the binary observer has a property of the chattering alleviation in the constant boundary layer. However, the steady state estimation accuracy and robustness are dependent upon the width of the constant boundary. In order to improve the steady state performance of the binary observer, the binary observer is formed by adding extra integral dynamics to the switching hyperplane equation. Also, because the conventional fixed gain PI controller are very sensitive to step change of command speed, parameter variations and load disturbance, the fuzzy logic controller is used to compensate a fixed gain PI controller. Therefore, a gain PI is fixed and the IPMSM is drived at another speed region. The effectiveness of the proposed the adaptive integral observer and the fuzzy logic controller are confirmed by experimental results.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.2
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• pp.106-110
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• 2001

The external force disturbance is the one of the main causes that deteriorate the performance of the magnetic suspension. Thus, this paper develops a fuzzy estimator for gain scheduling control of magnetic suspension system suffering from the unknown disturbance. The propose fuzzy estimator computes the disturbance injected to the plant the gain scheduled controller generates the corresponding stabilizing control input associated with estimated disturbance. In the simulation results we confirm the novelty of the proposed control scheme comparing with the other method using a feedback linearization.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.78-83
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• 2003

In this paper, it is dealt with a controller design problem for nonlinear systems with higher order relative degree. A robust adaptive control for uncertain nonlinear systems with stable zero dynamics will be proposed based on the high-gain adaptive output feedback and backstepping strategies. The proposed method is useful in the case where only the output signal is available.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.149-159
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• 2008

This paper considers the problem of adaptive fault-tolerant guaranteed cost controller design via dynamic output feedback for a class of linear time-delay systems against actuator faults. A new variable gain controller is established, whose gains are tuned by the designed adaptive laws. More relaxed sufficient conditions are derived in terms of linear matrix inequalities (LMIs), compared with the corresponding fault-tolerant controller with fixed gains. A real application example about river pollution process is presented to show the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1092-1099
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• 2000

An adaptive array system can effectively remove all received interferences by using adaptive algorithms even though the received signal condition is not known. The conventional adaptive array systems, however, cannot remove all interferences adaptively and converge very slowly when the eigenvalue spread of the input covariance matrix is large. In the paper, a new adaptive array system called an automatic gain controller (AGC) Applebaum array and its control algorithm are proposed to overcome the performance degradation of conventional Applebaum array in multiple interference conditions. The performance analysis of the proposed AGC Applebaum array is described under the condition of multiple narrowband interferences. Simulation results show the array output signal-to-noise ratio (SNR) of the AGC Applebaum array increases by 30dB compared to that of the conventional Applebaum array in the simulation condition. The gain of the AGC Applebaum array in the incident direction of a weaker interference is also shown to be lower than that of the conventional Applebaum array.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1539-1544
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• 2005

The gauge control of the fishing mill is very important because more and more accurately sized hot rolled coils are demanded by customers recently. Because the mill constant and the plasticity coefficient vary with the specifications of the mill, the classification of steel, the strip width, the strip thickness and the slab temperature, the variation of these parameters should be considered in the automatic gauge control system(AGC). Generally, the AGC gain is used to minimize the effect of the uncertain parameters. In a practical field, operators set the AGC gain as a constant value calculated by FSU (Finishing-mill Set-Up model) and it is not changed during the operating time. In this paper, the thickness data signals that occupy different frequency bands are respectively extracted by adaptive filters and then the main cause of the thickness variation is analyzed. Additionally, the AGC gain is adaptively tuned to reduce this variation using the online tuning model. Especially ANFIS(Adaptive-Neuro-based Fuzzy Interface System) which unifies both fuzzy logics and neural networks, is used for this gain adjustment system because fuzzy logics use the professionals' experiences about the uncertainty and the nonlinearity of the system. Simulation is performed by using POSCO's data and the results show that proposed on-line gain adjustment algorithm has a good performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.499-505
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• 2011

In this paper, we proposed an adaptive fuzzy sliding mode control for nonlinear systems without parameter projection method. By modifying the controller structure, the parameters of the estimated input gain function are guaranteed not being identically zero and it is shown that the control scheme will not cause any implementation problem even if the estimated value of input gain function is zero at any moment during on-line operations. Except for the input gain function which an approximate estimate for its lower bound is needed, the proposed control scheme does not assume a priori the exact values of the bounding parameters. Based on Lyapunov synthesis methods, the overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. This can be illustrated by the simulation results for an inverted pendulum system.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.33-42
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• 2003

Kuo et al proposed an on-line method for an adaptive prediction error filter for improving secondary path modeling performance in the modeling method of the secondary path. This method have some disadvantages, namely having to use additive noise with the result that noise control performance is not good since it is focused on the estimated performance of the secondary path. In this paper, we proposes a modified Kuo model using gain control parameter and delay. It uses a reference signal for additive noise to improve the problems in the existing Kuo model.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.4-40
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• 2001

This paper addresses the problem of designing robust adaptive output tracking control for a class of MIMO nonlinear systems which have different number of inputs and outputs The stability of the whole closed-loop system is guaranteed in the sense of Lyapunov and uniformly Itimately boundedness of the tracking error vector as well as estimated parameters are shown. In addition, we show that the restrictive assumptions on input gain matrix which is presumed in the past works can be eliminated by using proposed control law.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.283-292
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• 1996

Automatic guidance of farm tractors would improve productivity by reducing operator fatigue and increasing machine performance. To control tractors within $\pm$5cm of the desired path, fuzzy and adaptive steering controllers were developed to evaluate their characteristics and performance. Two input variables were position and yaw errors, and a steering command was fed to tractor model as controller output. Trapezoidal membership functions were used in the fuzzy controller, and a minimum-variance adaptive controller was implemented into the 2-DOF discrete-time input-output model. For unit-step and composite paths, a dynamic tractor simulator was used to test the controllers developed. The results showed that both controllers could control the tractor within $\pm$5cm error from the defined path and the position error of tractor by fuzzy controller was the bigger of the two. Through simulations, the output of self-tuning adaptive controller was relatively smooth, but the fuzzy controller was very sensitive by the change of gain and the shape of membership functions. Contrarily, modeling procedure of the fuzzy controller was simple, but the adaptive controller had very complex procedure of design and showed that control performance was affected greatly by the order of its model.