• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.859-861
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• 1999

This paper describes the development of a fuzzy gain scheduling scheme of PID controller for inverted pendulum system. Fuzzy rules and reasoning are utilized on-line to determine the controller parameters based on the error signal and its difference. Simulation results demonstrate that better control performance can be achieved in comparison with PID controller using pole placement to control of a Inverted pendulum.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.257-262
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• 1994

This paper describes an autotuning fuzzy PID controller for a position control of DC serve motor. Because ZNM(Ziegler-Nichols Method) with relay feedback has the difficulty in re-tuning the PID parameters and adaptive method has complex algorithm, a new method to overcome those problems is required. The proposed scheme determines the initial PID gains by using ZNM with relay feedback, and then re-tunes the optimal PID parameters by using fuzzy expert system whenever control conditions are changed. To show the validity of the proposed method, a position control of DC servo motor is illustrated by computer simulation and is experimented by a designed controller.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.195-195
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• 1999

This paper is Proposed a new method to deal with the optimized auto-tuning for the PID controller which is used to the process-control in various fields. First of all, in this method, 1st order delay system with dead time which is modelled from the unit step response of the system is Pade-approximated, then initial values are determined by the Ziegler-Nickels method. So we can find the parameters of PID controller so as to minimize the fuzzy criterion function which includes the maximum overshoot, damping ratio, rising time and settling time. Finally, after studying the parameters of PID controller by Backpropagation of Neural-Network, when we give new K, L, T values to Neural-Network, the optimized parameter of PID controller is found by Neural-Network Program.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.705-709
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• 2005

In this paper, we introduce fuzzy algorithm similar to human's way of thinking and designed collision detection system of vehicles. First, before the model vehicles design, we did simulation collision detection using PID and Fuzzy Controller. As a result, P.O that is Percent Overshoot when make use of PID controller happened from smallest 32% to 45%. But, In case of using fuzzy controller they produced about 10% in 7% in case use 25 rule. We designed model vehicles that introduce Auto Guided Vehicle(AGV) with confirmed result in simulation. We set Polaroid 6500 sensor on the front of model automobile because distinguish existence automobile to the head. And we composed motor drive part to run vehicles and 80C196KC processor for control movement of vehicles influenced on distance data of the front vehicles that receive from supersonic waves sensor. In case of using Fuzzy controller, last value percent error happened about maximum 15% in smallest 5%, and we confirmed that distance with front vehicles kept when state hold time is about maximum 16 seconds in smallest 10 seconds.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.396-406
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• 2007

In this paper, we proposed development of improved model reduction and design of hierarchical controller using reduction model. The model reduction is considered that it is the transient response and the steady-state response through the use of nyquist curve. The hierarchical controller selected tuning of PID controller to ensure specified gain and phase margin and hybrid smith-predictor fuzzy controller using reduction model. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.70-78
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• 1997

Proportional-integral-derivative(P1D) controllers have been still widely used in industrial processes due to their simplicity, effectiveness, robustness for a wide range of operating conditions, and the familiarity of control engineers. And a number of recent papers in fuzzy systems are showing that fuzzy systems are universal approximators. That is, fuzzy controllers are capable of approximating any real continuous function on a compact set of arbitrary accuracy. In this paper, we derive the linear PID control law from the fuzzy control algorithm where all fuzzy sets for representing plant state variables and a control variable use common triangular types. We first lead a linear PD control law from a fuzzy logic control with only two fuzzy sets for error and change-of-error. And then we derive the linear PID control law from a fuzzy controller. We here assumed that the intervals of error, change-of-error, and integral error could be partitioned into arbitrary numbers, respectively. As a result, a linear PID controller is only a sort of various fuzzy logic controls.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.650-652
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance. FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and increase efficiency, a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy Pi and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and them. The resultant rule base is Macvicar-Whelan type. The frequency response information is used in tuning of membership functions. Also a tuning strategy for the scaling factors is Proposed based on the relationship between PID gain and them. Simulation results show better performance and the effectiveness of the proposed method.

• Ocean Systems Engineering
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• v.3 no.3
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• pp.237-255
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• 2013

In many applications, Remotely Operated Vehicles (ROVs) are required to be capable of course keeping, depth keeping, and height keeping. The ROV must be able to resist time-variant external forces and moments or frequent manipulate changes in some specified circumstances, which require the control system meets high precision, fast response, and good robustness. This study introduces a Fuzzy-Incomplete Derivative Ahead-PID (FIDA-PID) control system for a 500-meter ROV with four degrees of freedom (DOFs) to achieve course, depth, and height keeping. In the FIDA-PID control system, a Fuzzy Gain Scheduling Controller (FGSC) is designed on the basis of the incomplete derivative ahead PID control system to make the controller suitable for various situations. The parameters in the fuzzy scheme are optimized via many cycles of trial-and-error in a 10-meter-deep water tank. Significant improvements have been observed through simulation and experimental results within 4-DOFs.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.1-5
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• 2000

The conventional PID controller has been extensively used to speed control of marine diesel engines. However one of drawbacks is that its control performance can be degraded if the parameters are fixed on whole operating points. In this paper a scheme for integrating PID control and the fuzzy technique is presented to control speed of a marine diesel engine on whole operating points. At first the PID controller is designed at each speed mode whose parameters are optimally adjusted using a genetic algorithm, Then fuzzy "if-then" rules combine the controllers as a consequence part. To demonstrate the effectiveness of the proposed fuzzy controller a set of simulation works on a marine diesel engine are carried out.rried out.