• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.12-17
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• 1991

In this paper, a variable structure system control method is proposed to the trajectory control of robot arm. A proposed method uses nonlinear switching function and saturation function. Furthermore, learning control method uses to decrease of the following error. The computer simulation results show that the chattering and the following error decrease and is improved the control the performance by a proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.265-272
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• 2013

This paper addresses a control allocation method for fault-tolerant control by redistributing redundant control surfaces. The proposed method is based on a classical daisy chain approach for the compensation of faulty actuators. The existing daisy chain method calculates a desired moment according to a number of actuator groups. However, this method has a significant limitation; that is, any faulty actuator belonging to the last actuator group cannot be compensated, since there is no more redundant actuator group that can be used to generate the required moments. In this paper, a modified daisy chain method is proposed to overcome this problem. Using the proposed method, the order of actuator groups is readjusted so that actuator groups containing any faulty actuator are always placed in an upper group instead of the last one. A set of simulation results with an F-18 HARV aircraft demonstrate that the proposed method can achieve better performance than the existing daisy chain method.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.253-259
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• 2014

This study proposes a map-based control method to improve a vehicle's lateral stability, and the performance of the proposed method is compared with that of the conventional model-referenced control method. Model-referenced control uses the sliding mode method to determine the compensated yaw moment; in contrast, the proposed map-based control uses the compensated yaw moment map acquired by vehicle stability analysis. The vehicle stability region is calculated by a topological method based on the trajectory reversal method. The performances of model-referenced control and map-based control are compared under various road conditions and driving inputs. Model-referenced control uses a control input to satisfy the linear reference model, and it generates unnecessary tire lateral forces that may lead to worse performance than an uncontrolled vehicle with step steering input on a road with low friction coefficient. The simulation results show that map-based control provides better stability than model-referenced control.

• Smart Structures and Systems
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• v.10 no.1
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• pp.15-31
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• 2012

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.576-585
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• 2016

The current control methods of Y-connected 7 Phase BLDC motor are sine wave current control and square wave control. The sine wave current control method needs dq axis transformation of $7{\times}7$ matrix for current control and very complex. Also this method is not suitable for multi Phase BLDC motor of trapezoidal back emf wave. Therefore, in Y connected multi phase BLDC motor, the square wave current control methods are required. Generally, in the 3Phase BLDC system, Average current control method is used for current control. The average current is obtained that the summation of absolute value of each phase current magnitude is divided by the number of conduction phase. However, if average current control method is applied to multi-phase system, there is a problem that each phase currents are different. This problem affects unbalance of each phase torque and fluctuation of total torque. This paper proposed each phase current control method of Y connected 7Phase BLDC system. Proposed method is used for PI controller of each phase for each phase current control. This method can perfect square wave current control. Also, configuration of the method is easier than DQ axis transformation. Proposed method is verified through simulation and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.552-555
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• 1997

Recently, increasing attention has been paid to the application of learning control method to robot manipulator control. Because the learning control method does not require an exact dynamic model, it is flexible and easy to implement. In this paper, we implement a learning control scheme which consists of a unique feedforward learning controller and a linear feedback controller. The learning control method does not require acceleration terms that are sensitive to noise and has the capability of rejecting unknown disturbances and adapting itself to time-varying system parameters. The feasibility of the learning control scheme is soon by implementing the control scheme to a commercial robot manipulator and the performance of which is also compared with the conventional linear PID control method.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.20-26
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• 1996

Recently, for the speed control of a diesel engine, some methods using the modern control theory such as LQ control technique, or $\textit{H}_{\infty}$control theory etc., have been reported. However, most of speed controlers of a diesel engine ever developed are still using the PID control algorithm. And, as another approach to the speed control of a diesel engine, the authors proposed already a new method to adjust the parameters of the PID controller by a model matching method. In the previous paper, the authors confirmed that the proposed new method is superior to Ziegler & Nichols's method through the analysis of results of the digital simulations under the assumption that the parameters of a diesel engine are known exactly. But, actually, it is very difficult to find out the value of parameters of a diesel engine accurately. And the parameters of a diesel engine are changigng according to the operating condition of a diesel engine. So, in this paper, a method to estimate the parameters of the PID controller for the speed control of a diesel engine by means of the model matching method are proposed. Also, the digital simulations are carried out in cases either with or without measurement noise. And this paper confirms that the proposed method here is superior to Ziegler & Nichols's method through the analysis of the characteristics of indicial responses.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.755-762
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• 2022

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• Journal of Power Electronics
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• v.14 no.3
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• pp.549-563
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• 2014

In this paper, the speed regulation problem of permanent synchronous motor (PMSM) systems under the vector control framework is studied. A model reference adaptive controller (MRAC) based on the Lyapunov stability theory is first designed. Since the standard MRAC method provides poor disturbance rejection performance in the case of strong disturbances, a composite control method which combines the MRAC method and the disturbance estimation method, called the MRAC+ESO method, is proposed. An extended state observer (ESO) is introduced to estimate the lumped disturbances. The obtained estimated value acts as a feedforward compensation term to the MRAC controller. A stability analysis of the composite control method is given. Simulation and experimental results are presented and compared to show the effectiveness of the proposed control method.