• Proceedings of the KIPE Conference
• /
• 2000.07a
• /
• pp.460-463
• /
• 2000

Fuzzy logic control(FLC) has been studied extensively and has been applied in various applications. The most popular control strategy takes the Fuzzy Proportional-Integral(FPI) form while systematic methods have been developed to derive the fuzzy rules and membership functions the choice of the scaling factors remains an open problem, In this paper an analytical FPI scaling factor determining method is derived based on the functional equivalence of the PI and FPI controllers. Simulation have been carried out with a brushless DC motor drive system as test-bed the obtained results drive system as test-bed the obtained results have verified that the derived method is applicable to both the initial choice and further tuning of the FPI scaling factors.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.156-160
• /
• 1997

In this paper, the control scheme is proposed that PI controller parameter used for TAH speed control is adapted by fuzzy logic method using only the motor current waveform. By scheduling PI parameters, minimization of the vibration and the energy consumption and overcoming AoP loads becomes possible. In in vitro tests experimental results show our approach is a good scheme that is adapted to changing afterloads well.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.2
• /
• pp.166-172
• /
• 2003

The dynamics of the DDWMR depends on the velocity difference of the two driving wheels. And which is known as a type of non-holonomic equation. By this reason, the treatment of DDWMR had become difficult and conservative. In this paper, the differential-driving wheeled mobile robot is considered. The Takaki-Surgeno fuzzy model and a control method for DDWMR is presented. The suggested controller has three control elements. The first element is fuzzy state feedback designed for eliminating the dependence of time-varying parameter. The second element is weighting controller which is designed for good frequency response. The third controller is PI-controller which is designed for good command following and robustness with un-modeled dynamics. In order for achieving the performance objective, the design of controller is based on the loop-shaping algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.5 no.3
• /
• pp.64-72
• /
• 1995

This paper proposes how to tune the gains of PI controllers in case of gain change in a process control system. Controllers of PI type have been used in industry and the gains of the controllers have been tuned by expert engineers. It, therefore, takes much time and efforts to tune the controllers. It is more difficult to find gains of multi-loop processes. The tuning method of a fuzzy tuner in this paper is developed based on the assumptions that the PI controllers are of analog type and are tuned off-line, and that the characteristic values must be supplied for the tuner. A Tuner using Fuzzy Logic(FLT1 is capable of showing presentlpast states of a process control system and finding gains of PI controllers. The verfication of the FLT is shown by various experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.265-271
• /
• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.617-624
• /
• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.120-125
• /
• 2004

In the rolling of steel or non-steel metal the most important quality aspect are thickness and flatness. In thickness, there are two important factors. One of them is getting close with accurate goal, nominal gauge, the other is minimize gauge bandwidth, the variation in gauge. In this thesis, we proposed the fuzzy model AGC to minimize gauge variation along the length, developed the rolling mill dynamic model using the math mode of the rolling mill process and the rolling model related with the variety character of the rolling material. We compared the gauge control efficiency of fuzzy model AGC and PI mass flow AGC. We have got a simulation result, that the exit gauge variation of PI mass flow AGC was 2 micron and fuzzy model AGC was 1.2 micron at 1200mpm of rolling speed when each controller was rolling 5 micron of material that is the entry gauge variation.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.177-177
• /
• 2000

In this paper, we propose a two-input two-output fuzzy controller to improve the performance of transient response and to eliminate the steady state error. The outputs of this controller are the control input calculated by position-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted on-line by fuzzy rules according to the current trend of the controlled process. To show the usefulness of the proposed controller, it is applied to several systems that are difficult to get satisfactory response by conventional PD controllers or PI controllers.

• Proceedings of the KIPE Conference
• /
• 2010.11a
• /
• pp.276-277
• /
• 2010

This paper describes the control of a utility-connected doublefed induction machine (DFIM) for wind power generation systems (WPGS). Real and reactive powers (PQ) at the stator side of DFIM are strictly controlled to supply the power to the grid without any problems. In this paper the control is realized using Fuzzy PI controller based on the stator-flux orientation control.

• Journal of IKEEE
• /
• v.2 no.2 s.3
• /
• pp.299-308
• /
• 1998

In this paper, reliable switched reluctance motor(SRM) drive system and 4-rule based fuzzy logic modifier(FLM) of the conventional PI controller are presented. The i80C196KC, low-cost one-chip microcontroller is used for designing SRM drive controller which include the speed controller and the starting sequence. The fuzzy logic modifier which exhibits a stabilizing effects on the closed-loop system, has good robustness regarding the improperly tuned PI controller. The simulation and experimental results are performed to verify the capability of proposed control method on 6/4 salient type SRM.