• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.29-38
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• 1998

This paper proposes the neural network self-tuning PID controller for the load frequency control of 2- areas power system, namely, the prompt convergence of frequency and tie-line power flow deviation. The neural network applied to computer simulation consists of neurons of two inputs, ten hiddens and tliree outputs layer. Neurons of two inputs layer receive the error and its change rate of the system and cutputs layer consists of three neurons for the parameters of the PID controller. The simulation results shows that the proposed neural network self-tuning PID controller is superior to conventional control t~:chniques(Optimal, PID) in dynamic response and control performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.9
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• pp.2153-2161
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• 1993

Self-tuning control algorithms for an input-amplitude constrained system are developed and implemented. Magnitude of control input for small motors is generally restricted to narrow bound due to actuator saturation. The gain-adjusted control algorithm and the bounded-gain control algorithm proposed in this study yield smoother control input variations within the magnitude constraints comparing with the existing Clarke's suboptimal control algorithm. In the gain-adjusted control algorithm, the feedforward gain is adjusted using maximum gain, while in the bounded-gain control algorithm, the feedforward gain is bounded using weighting factor. For the DC servo motor control, the system performances of the proposed algorithms are compared with those of the existing algorithm by computer simulation and experiment. It is shown that the input variations of the proposed algorithms are smoother as compared with the existing algorithm.

• International Journal of Ocean System Engineering
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• v.3 no.1
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• pp.16-21
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• 2013

This paper presents a design of fuzzy PD depth controller for the autonomous underwater vehicle entitled KAUV-1. The vehicle is shaped like a torpedo with light weight and small size and used for marine exploration and monitoring. The KAUV-1 has a unique ducted propeller located at aft end with yawing actuation acting as a rudder. For depth control, the KAUV-1 uses a mass shifter mechanism to change its center of gravity, consequently, can control pitch angle and depth of the vehicle. A design of classical PD depth controller for the KAUV-1 was presented and analyzed. However, it has inherent drawback of gains, which is their values are fixed. Meanwhile, in different operation modes, vehicle dynamics might have different effects on the behavior of the vehicle. In this reason, control gains need to be appropriately changed according to vehicle operating states for better performance. This paper presents a self-tuning gain for depth controller using the fuzzy logic method which is based on the classical PD controller. The self-tuning gains are outputs of fuzzy logic blocks. The performance of the self-tuning gain controller is simulated using Matlab/Simulink and is compared with that of the classical PD controller.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1080_1081
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• 2009

This paper proposes a cooling system using self tuning PI controller for improving the output of BIPV module. The temperature characteristics in regard to improving the output of BIPV system has rarely been studied up to now but some researchers only presented the method using a ventilator. The cooling system efficiency of BIPV module applied to a ventilator mainly depends on the weather such as wind and insolation etc. Because the cooling system of BIPV module using a ventilator is so sensitive, that is being set off by wind speed at all time but is unable to operate in the nominal operating cell temperature(NOCT) which is able to make the maximum output. The paper proposes the cooling system using thermoelectron by self tuning PI controller so as to solve such problems. The thermoelectron control of self tuning PI controller can be controlled independently in the outside environment because that is performed by micro-controller. The temperature control of thermoelectron, also, can be operated around NOCT through algorism of the temperature control. Therefore, outputs of the whole system increase and the efficiency rises. The paper demonstrates the validity of proposed method by comparing the data obtained through a experiment of the cooling method of BIPV using a ventilator and proposed thermoelectron

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.192-197
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• 2007

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.63-66
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• 1997

The rule bases self organizing controller(SOC) has one of its main advantages in the fact that there is no need to have a mathematical description of the system to be controlled. In this controller, the rules are linguistics statements expressed mathematically through the concepts of fuzzy sets and correspond to the actions a human operator would take when controlling a given process. With this controller, we have performed to sight stabilization system, and we realize that it needs a scale factor tuning. The self tuning controller(STC) uses an instantaneous system fuzzy performance which can give an inspection to the scale factor. Therefore, the STC can compensate the scale factor when it is not adequately tuned. With this trial, we shows that STC can give a good transient characteristics in the nonlinearity which imposed basically in the conventional servo system.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.72-72
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• 2000

This paper presents a methodology combining sliding mode control and fuzzy control to tune the boundary layer and input gain according to the system state. The equivalent control is designed such that the nominal system exhibits desirable dynamics, The robust control with fuzzy self-tuning is then developed to guarantee the reaching condition and reduce chattering phenomenon in the presence of parameter and disturbance uncertainties.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.122-132
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• 1993

In this paper, a self tuning control algorithm is proposed for the high performance drive of DC servo motor, which is adequate to the servo system having frequent load variation. In order to realization of the algorithm, the control system is developed using a fixed point high speed digital signal processor. TMS320C25. Control algorithm is composed of two parts. One is estimation law part using recursive least mean square method, the other is control law part using minimum variance control method. For the purpose of easiness of applying adaptive algorithm, developed control system is based o PC-DSP structure which can develop, debug programs and monitor the dynamic behaviors,etc. Through computer simulation and experimental results, it was verified that proposed control system could estimate system parameters and was robust to the variation of the load and as a result, was adequate to the servo motor drives.