• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• 대한전기학회논문지
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• 제43권5호
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• pp.819-826
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• 1994

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy sets. Each fuzzy set is characterized by a membership function. The tuning fuzzy controller has paramenters that are input/output scaling factors to effect control output. In this paper we propose a tuning method for the scaling factor Computer simulations carried out on first-order and second-order processes will show how the present tuning approach improves the transient and the steady-state characteristics of the overall system.The applicability of the proposed algorithm is certified by computer simulation results.

• 제어로봇시스템학회논문지
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• 제2권1호
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• pp.14-20
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• 1996

This paper presents a self tuning method of a velocity type PID controller for minimum or non-minimum phase systems with time delays. The velocity type PID control structure is determined in the process of minimizing the variance of the auxilliary output, and self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a design parameter. This method is simple and effective compared with other existing methods[1,2]. Numerical examples are included to illustrate the procedure and to show the performance of the control system.

• Journal of Mechanical Science and Technology
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• 제15권5호
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• pp.580-591
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• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 전력전자학술대회 논문집
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• pp.243-246
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• 2001

The control of the SRM(Switched Reluctance Motor) is usually based on the non-linear inductance profiles with positions. So determination of optimal switching angle is very different. This paper proposed that the determination method of turn-on/off angle in the SRM drives is to maintain the high torque, which is realized by using self-tuning control method. During the sampling time, a number of pulses from the encoder are checked by using micro-controller. And compared with pre-checked a number of pulses. After calculating difference between two data, turn-on/off angle moves forward or backward direction by using self-tuning method. The optimal turn-on/off angle is determined by iterating such a process and the maximum torque is maintained. Experimental results are provided to demonstrate the validity of the self-tuning controller.

• 제어로봇시스템학회논문지
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• 제5권8호
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 전문대학교육위원
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• pp.155-157
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• 2005

This paper shows how to design a MPPT control of PV system using neural network PI self tuning. The conventional self-tuning methods have the voltage control problem of nonlinear PV system which can't adapt against any kinds of noise or operation circumstances. In this paper, supposed to solve these problem to PI parameters controller algorithm using ANN. In the proposed algorithm, the parameters of the controller were adjusted to reduce by on-line system the error of the output voltage of DC-DC chopper. In this process, EBPA NN was constituted to an output error value of a DC-DC chopper and conspired an input and output. The performance of the self-tuning controller is compared with that of the PI controller tuned by conventional method. The effectiveness of the proposed control method is verified thought the Matlab Simulink.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.442-445
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• 1997

We present a hybrid self-tuning method of fuzzy inference systems with hyper elliptic Gaussian membership functions using genetic algorithm(GA) and back-propagation algorithm. The proposed self-tuning method has two phases : one is the coarse tuning process based on GA and the other is the fine tuning process based on back-propagation. But the parameters which is obtained by a GA are near optimal solutions. In order to solve the problem in GA applications, it uses a back-propagation algorithm, which is one of learning algorithms in neural networks, to finely tune the parameters obtained by a GA. We provide Box-Jenkins time series to evaluate the advantage and effectiveness of the proposed approach and compare with the conventional method.

• Journal of Mechanical Science and Technology
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• 제18권3호
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• pp.379-387
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• 2004

A novel flatness sensing system which is called the Flatness Sensing Inter-stand Looper(FlatSIL) system is suggested and a self-tuning PI control system using the FlatSIL is designed for improving the flatness of hot strip in finishing mill processes. The FlatSIL system measures the tension along the direction of the strip width by using segmented rolls, and the tension profile is approximated through the tension of each segmented roll. The flatness control system is operated by using the tension profile. The proposed flatness control system as far as the tension profile-measuring device works for the full strip length during the strip rolling in finishing mills. The generalized minimum variance self-tuning (GMV S-T) PI control method is applied to control the flatness of hot strip which has a design parameter as weighting factor for updating the PI gains. Optimizing the design parameter in the GMV S-T PI controller, the Robbins-Monro algorithm is used. It is shown by the computer simulation and experiment that the proposed GMV S-T PI flatness control system has better performance than the fixed PI flatness control system.

• 제어로봇시스템학회논문지
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• 제8권3호
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• pp.199-207
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• 2002

In this paper, a self-tuning gain-scheduled skyhook control for semi-active suspension systems is investigated. The dynamic characteristics of a continuously variable damper including electro-hydraulic pressure control valves is analyzed. A 2-d.o.f. time-varying quarter-car model that permits variations in sprung mass and suspension spring coefficient is considered. The self-tuning skyhook control algorithm proposed in this paper requires only the measurement of body acceleration. The absolute velocity of the sprung mass and the relative velocity of the suspension deflection are estimated by using integral filters. The skyhook gains are gain-scheduled in such a way that the body acceleration and the dynamic tire force are optimized. An ECU prototype is discussed. Experimental results using a 1/4-ear simulator are discussed. Also, a suspension ECU prototype targeting real implementation is provided.