• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.519-520
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• 2016

BLSRM is a nonlinear, strong coupling and multi-variable system. The conventional control method is vulnerable to uncertain factors such as the load disturbance and satellite parameters change. It is difficult to obtain satisfactory control effect. Basing on a 8/10 BLSRM, whose suspending force control is separated with the torque control, this paper presents adaptive fuzzy PID controller for levitation control, which apply the fuzzy logic control to the conventional PID controller for parameters self-tuning. Both fuzzy and parameters of PID controller are self-tuning on-line, which improve the performance of controller. Finally, simulation and experimental results show the performance of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.5B
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• pp.442-448
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• 2006

This paper considers a home gateway(HG) that processes VOD services and controls home appliances. This paper proposes a PID algorithm-based adaptive bandwidth control method used in the HG, which guarantees QoS of incoming traffic such as VOD and real-time control data via control of outgoing traffic and have little effects on the CPU computation time. The proposed method is validated via implementation of real test environment.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1341-1343
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• 1996

This paper presents a design method for an on-line adaptive neural networks based intelligent controller. The proposed neural controller, assuming PID controller is initially presented, learns the equivalent behaviors of the existing PID controller initially and switches to take over the PID control system. Then, it executes on-line adaptation via evaluating its performance and minimizing user defined cost function constantly so that the optimal control can be achieved. The PID controller and the proposed neural controller are investigated and compared in computer simulation.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.139-148
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• 2024

In this paper, we propose a two-degree-of-freedom snake robot head system and an I-PID (Intelligent Proportional-Integral-Derivative)-based controller utilizing RBF (Radial Basis Function) neural network and adaptive robust terms as a control strategy to reduce rotation occurring in the snake robot head. This study proposes a two-degree-of-freedom snake robot head system to avoid complex snake robot dynamics. This system has a control system independent of the snake robot. Subsequently, it utilizes an I-PID controller to implement a control system that can effectively manage rotation at the snake robot head, the robot's nonlinearity, and disturbances. To compensate for the time delay estimation errors occurring in the I-PID control system, an RBF neural network is integrated. Additionally, an adaptive robust term is designed and integrated into the control system to enhance robustness and generate control inputs responsive to signal changes. The proposed controller satisfies stability according to Lyapunov's theory. The proposed control strategy was tested using a 9-degreeof-freedom snake robot. It demonstrates the capability to reduce rotation in Lateral undulation, Rectilinear, and Sidewinding locomotion.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.2
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• pp.105-110
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• 1990

In this paper, a compliance robot control algorithm using a PID adaptive controller is proposed. The compliance robot is suitable for the tasks in contact with environment, such as assembly operation or surface processing. A hybrid robot control method can control force and position simultaneously and two independant feedback closed loops are formed in this method. Because the compliance robot is operated in contact with environment, it is very difficult to obtain linear model of dynamics for this robot. In order to overcome this difficulty, a PID adaptive controller independant of robot dynamics is applied to the compliance robot. The proposed control algorithm for the compliance robot was analyzed and conformed by simulating the surface processing task by a two-joint robot.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.326-331
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• 2016

This paper presents a novel robust adaptive AC8B exciter system against synchronous generators for ships. A PID (proportional integral derivative) control framework, which is a part of the AC8B exciter system, is simply composed of nominal and auxiliary control configurations. For selecting these proper parameter values, the former is conventionally chosen based on the experience and knowledge of experts, and the latter is optimally estimated via a neural networks optimization procedure. Additionally, we propose an online parameter learning-based auxiliary control to practically cope with deterioration of control performance owing to uncertainty in electric generator systems. Such a control mechanism ensures the robustness and adaptability of an AC8B exciter to enhance control performance in real-time implementation. We carried out simulation experiments to test the reliability of the proposed robust adaptive AC8B exciter system and prove its superiority through a comparative study in which a conventional PID control-based AC8B exciter system is similarly applied to our simulation experiments under the same simulation scenarios.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.11
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• pp.720-726
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• 1988

This paper deals with an applicaton of some adaptive algorithms to a heat exchanger using a microcomputer and reviews the experimental results obtained. The heat exchanger prepared for experments was identified as a non-minimum phase system and its exact mathematical models was hardly obtainable with direct computation. Thus, classical strategies, such as PID, needed many trial and errors to determine parameters of the controllers. Furthermore such strategies could not guarantee good performance when system parameters vary. To overcome these difficulties and improve performance, two adaptive methods applicable to a non-minimum phase system were chosen and put to the test. In this paper the performance of adaptive controllers is compared with that of conventional PID controller. The final objective of this paper is to construct a controller readily applicable to industrial processes using a microprocessor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.5
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• pp.225-234
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• 2000

In this paper, we present an nonlinear PID controller with network based compensator which consists of a conventional PID controller that controls the linear components and neuro-compensator that controls the output errors and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the output errors through the neuro-compensator. Various simulations and comparative studies have proven that the proposed nonlinear PID controller produces superior results to other existing PID controllers. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.3
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• pp.134-141
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• 2004

Recently, neural network techniques are widely used in adaptive and learning control schemes for production systems. However, in general it takes up a lot of time to learn in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult for the PID gains suitably, lots of researches have been reported with respect of turning schemes of PID gains. A neural network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed neural network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accidents.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.