• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.677-680
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• 2003

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. we present self-tuning control of SRM for maximum torque and efficiency with phase current and shaft position sensor During the sample time, micro-controller checks the number of pre-checked pulse. After micro-controller calculates between two data, it move forward or backward turn-off angle. When the turn-off angle is fixed optimal turn-off angle, turn-on angle moves forward or backward by a step using self-tuning control method. And then, optimal turn-off angle is searched once again. As such a repeating process, turn-on/off angle is moves automatically to obtain the maximum torque and efficiency. The experimental results are presented to validate the self-tuning algorithm.

• Proceedings of the KIEE Conference
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• 2005.10a
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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.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.76-78
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• 1987

This paper describes the theory and application of a multi-input/multi-output self-tuning regulator where the control objective is the assignment of the closed-loop pole set to prespecified locations. The algorithm described In this paper has a 'self-tuning' property. This self-tuners are more robust than the tuners that are based on optimal control synthesis method. This paper demonstrate usefulness of the algorithms by means of some simulation studies.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.3
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• pp.87-95
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• 1998

This paper presents the method for the automatic tuning of a design weighting polynomial parameter of a generalized minimum-variance stochastic self tuning controller which adapts to changes in the system parameters with time delays and noises. The 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 weighting polynomial parameters. The proposed self tuning method is simple and effective compared with other existing self tuning methods. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1577-1586
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• 2017

The Proportional-Integral-Derivative (PID) controller is still the most widespread control strategy in the industry. PID controllers have gained popularity due to their simplicity, better control performance and excellent robustness to uncertainties. This paper presents the optimal tuning of a PID controller for domestic induction heating systems with a series resonant inverter for controlling the induction heating power. The objective is to design a stable and superior control system by tuning the PID controller with a derivative filter (PIDF) through Fuzzy logic. The paper also compares the performance of the Fuzzy PIDF controller with that of a Ziegler-Nichols PID controller and a fine-tuned PID controller with a derivative filter. The system modeling and controllers are simulated in MATLAB/SIMULINK. The results obtained show the effectiveness and superiority of the proposed Fuzzy PID controller with a derivative filter.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.190-192
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• 1987

In the paper an multivariable decoupling self-tuning algorithm is proposed for controller design, by specifying the closed-loop behaviour of the system in the form of a reference model, so that the controller parameters can be estimated on-line as the process development. The effectiveness of this algorithm in controlling multivariable systems is demonstrated by simulation example in spite of the usual implementation problems of self-tuning controllers.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.179-181
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• 1987

In this paper, a self-tuning controller design is proposed by using pole-zero placement method and considering a system time delay. To got better tracking for the generalized self-tuning controller, pole placement method for the closed loop system and zero placement method for the error transfer function are Introduced. The proposed method shows better efficiency than pole placement method for minimizing tracking error. Simulation gives good results in tie reference signal tracking.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.47.6-47
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• 2001

In this paper a new indirect robust self-tuning regulator is proposed including an inverse system of a plant and a robust compensator such that it achieves the desired frequency shape specified by solving the mixed H$\infty$ sensitivity problem within a prescribed tolerance in the H$\infty$ norm. Consequently, in the proposed self-tuning regulator, robust stability is guaranteed in spite of the identification error.