• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.109-112
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• 2005

This paper presents an optimal and robust PI-PD controller design method for the second-order systems both with dead time and without dead time to satisfy the design specifications in the time domain via LQR design technique. The optimal tuning method of PI-PD controller are also developed by setpoint weighting and neural networks. It is shown that the simulation results show significantly improved performance by proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.276-280
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• 1992

In the paper, an optimal tuning algorithm is presented to automatically improve the performance of a hybrid controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of a hybrid controller, according to the change rate and limitation condition of output, The controller is applied to plants with time-delay. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.830-833
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• 1993

In this paper, an optimal tuning Algorithms is presented to automatically improve the performance of fuzzy controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of fuzzy controller, according to the change rate and limitation condition of output. The controller is applied to plants with dead time. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.9
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• pp.1618-1623
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• 2008

In this paper, a new design method for IMC-PID that adds a phase scaling factor of system identifications to the standard IMC-PID controller as a control parameter is proposed. Based on analytically derived frequency properties such as gain and phase margins, this tuning rule is an optimal control method determining the optimum values of controlling factors to minimize the cost function, integral error criterion of the step response in time domain, in the constraints of design parameters to guarantee qualified frequency design specifications. The proposed controller improves existing single-parameter design methods of IMC-PID in the inflexibility problem to be able to consider various design specifications. Its effectiveness is examined by a simulation example, where a comparison of the performances obtained with the proposed tuning rule and with other common tuning rules is shown.

• 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.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.705-707
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• 1997

An optimal tuning algorithm of scaling factors is presented in this paper to automatically improve the performance of fuzzy controller. Especially, fuzzy controller has determined an moderate Scaling factor through trial and error. The presented method estimates automatically the optimal values of I/O scaling factors, using modified steepest descent method and this optimal tuning is for nonlinear input/output scaling factors. Simulation results verify the validity of the presented method.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.575-591
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• 2006

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network 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. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.213-217
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• 2008

In this paper, a new design method for IMC-PID that adds a phase scaling factor of system identifications to the standard IMC-PID controller as a control parameter is proposed. Based on analytically derived frequency properties such as gain, phase margin and maximum magnitude of sensitivity function, this tuning rule is an optimal control method determining the optimum values of controlling factors to minimize the cost function, integral error criterion of the step response in time domain, in the constraints of design parameters to guarantee qualified frequency design specifications. The proposed controller improves existing single-parameter design methods of IMC-PID in the inflexibility problem to be able to consider various design specifications. Its effectiveness is examined by a simulation example, where a comparison of the performances obtained with the proposed tuning rule and with other common tuning rules is shown.

• 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.