• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.509-517
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• 2001

To deal with non-linearities and time-varying characteristics of hydraulic systems, in this paper, the neuro-fuzzy controller has been introduced. This controller does not require and accurate mathematical model for the nonlinear factor. In order to solve general fuzzy inference problems, the input membership function and fuzzy reasoning rules are used for determining the controller parameters. These parameters are determined by using the learning algorithm. The control performance of the neuro-fuzzy controller is evaluated through a series of experiments for the various types of inputs while applying disturbances to the hydraulic system. The performance of this controller was compared with those of PID and PD controllers. From these results, We observe be said that the position tracking performance of neuro-fuzzy is better those of PID and PD controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.407-412
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• 1998

A design technique based on the root locus approach for the SISO (Single-Input Single-Output) systems using PID (Proportional-Integral-Derivative) ${\times}$ (n-1) stage PD as a controller for the n$\^$th/ order plant is presented. The controller is designed based on transient and steady state response specifications. This controller can be used instead of a conventional PID controller. The overall system is approximated as a stable and robust second order system. The desired performances are achieved by increase the gain of the controller. In addition, the controller gain can be adjusted to obtain faster response with a little overshoot. The simulation results show the merits of this approach.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.32-39
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• 1997

One of the most important performance criteria in tuning the gain of position loop controller for CNC machining center is the contour error. In this papre we analyze contour error in the linear and circular interpolations for the axis-matched and mismatched cases. To have small contour errors, it is necessary to set the P gain for each axis to be same. And the D gain should be much smaller than the P gain. Baded on the analysis in the frequency domain, we propose a gain tuning method for the P and PD controllers. We show that the PD controller is better than the P controller. The effectiveness of this method is demonstrated by experiments.

• Smart Structures and Systems
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• v.26 no.4
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• pp.469-480
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• 2020

This is the first research on the smart control and vibration analysis of a Graphene nanoplatelets (GPLs) Reinforced Composite (GPLRC) porous cylindrical shell covered with piezoelectric layers as sensor and actuator (PLSA) in the framework of numerical based Generalized Differential Quadrature Method (GDQM). The stresses and strains are obtained using the First-order Shear Deformable Theory (FSDT). Rule of the mixture is employed to obtain varying mass density and Poisson's ratio, while the module of elasticity is computed by modified Halpin-Tsai model. The external voltage is applied to sensor layer and a Proportional-Derivative (PD) controller is used for sensor output control. Governing equations and boundary conditions of the GPLRC cylindrical shell are obtained by implementing Hamilton's principle. The results show that PD controller, length to radius ratio (L/R), applied voltage, porosity and weight fraction of GPL have significant influence on the frequency characteristics of a porous GPLRC cylindrical shell. Another important consequence is that at the lower value of the applied voltage, the influence of the smart controller on the frequency of the micro composite shell is much more significant in comparison with the higher ones.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.855-858
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• 1995

A neural network has very simple construction (input, output and connection weight) and then it can be robusted against some disturbance. In this paper, we proposed a neuro-controller using a Multi-Layered neural network which is combined with PD controller. The proposed neuro-controller is learned by backpropagation learning rule with momentum and neuro-controller adjusts connection weight in neural network to make approximate dynamic model of DC Servo motor. Computer Simulation results show that the proposed neuro-controller's performance is better than that of origianl PD controller.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.67-76
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• 1995

In recent research on the fuzzy controller, the crisp type fuzzy controller model, in which the consequent part of the fuzzy control rules are crisp real numbers instead of fuzzy sets, due to its simplicity in calculation, has been widely used in various applications. In this paper we try to analyze the dynamical behavior of the crisp type fuzzy controller with both inference methods of min-max compositional rule and product-sum inference. The analysis reveals that a crisp type fuzzy controller behaves approximately like a PD controller.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.3-8
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• 2000

This paper describes a development of self tuning scheme for PI and PO type fuzzy controllers. The output scaling factor(SF) is adjusted on-line by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error and change of error for the controlled variable using the most natural and unbiased membership functions. Simulation results demonstrate the better control performance can be achieved in comparison with Ziegler-Nichols(Z-N) PID controllers.

• Journal of Advanced Research in Ocean Engineering
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• v.2 no.4
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• pp.192-201
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• 2016

This study examined a PD controller and its application to automatic berthing control of an unmanned surface vehicle (USV). First, a nonlinear mathematical model was established for the maneuvering of the USV in the presence of environmental forces. A PD control algorithm was then applied to control the rudder and propeller during an automatic berthing process. The algorithm consisted of two parts, namely the forward velocity control and heading angle control. The control algorithm was designed based on longitudinal and yaw dynamic models of the USV. The desired heading angle was obtained using the "line of sight" method. Finally, computer simulations of automatic USV berthing were performed to verify the proposed controller subjected to the influence of disturbance forces. The results of the simulation revealed a good performance of the developed berthing control system.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.510-523
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• 2022

Considering the problems of poor control effect, weak disturbance rejection ability and adaptive ability of two-wheeled self-balanced robot (TWSBR) systems on undulating roads, this paper proposes a fuzzy neural network active disturbance rejection controller (FNNADRC), that is based on fuzzy neural network (FNN) for online correction of active disturbance rejection controller (ADRC)'s nonlinear control rate. Firstly, the dynamic model of the TWSBR is established and decoupled, the extended state observer (ESO) is used to compensate dynamically and linearize the upright and displacement subsystems. Then, the nonlinear PD control rate and FNN are designed, and the FNN is used to modify the control parameters of the nonlinear PD control rate in real time. Finally, the proposed control strategy is simulated and compared with the traditional ADRC and fuzzy active disturbance rejection controller (FADRC). The simulation results show that the control effect of the proposed control strategy is slightly better than ADRC and FADRC.