• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1823-1833
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• 2006

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

• Journal of Power Electronics
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• v.13 no.1
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• pp.161-169
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• 2013

This paper proposes a novel control strategy for a unified power quality conditioner (UPQC) including a series and a shunt active power filter (APF) to compensate the harmonics in both the distorted supply voltage and the nonlinear load current. In the series APF control scheme, a proportional-integral (PI) controller and a resonant controller tuned at six multiples of the fundamental frequency of the network ($6{\omega}_s$) are performed to compensate the harmonics in the distorted source. Meanwhile, a PI controller and three resonant controllers tuned at $6n{\omega}_s$(n=1, 2, 3) are designed in the shunt APF control scheme to mitigate the harmonic currents produced by nonlinear loads. The performance of the proposed UPQC is significantly improved when compared to that of the conventional control strategy thanks to the effective design of the resonant controllers. The feasibility of the proposed UPQC control scheme is validated through simulation and experimental results.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.217-236
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• 1999

Engineering process control (EPC) is one of the techniques very widely used in process. EPC is based on control theory which aims at keeping the process on target. Statistical process control (SPC), also known as statistical process monitoring. The main purpose of SPC is to look for assignable causes (variability) in the process data. The combined SPC/EPC scheme is gaining recognition in the process industries where the process frequently experiences a drifting mean. This paper aims to study the difference between SPC and EPC in simple terms and presents a case study that demonstrates successful integration of SPC and EPC for a product in drifting industry. Statistical process control (SPC) monitoring of the special causes of a process, along with engineering feedback control such as proportional-integral-derivative (PID) control, is a major tool for on-line quality improvement.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.11
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• pp.1068-1076
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• 2004

Technology on the proportional integral (PI) control have grown rapidly owing to the needs for the robust capacity of the controllers from industrial building sectors. However, PI controller requires tuning of gains for optimal control when the outside weather condition changes. The present study presents the possibility of reinforcement learning (RL) control algorithm with PI controller adapted in the HVAC system. The optimal design criteria of RL controller was proposed in the Environment Chamber experiment and a theoretical analysis was also conducted using TRNSYS program.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.1
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• pp.75-82
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• 2002

In this paper, the robust PID(Proportional-Integral-Derivative) controller was designed for speed control of BLDC motors using the frequency region model matching method. It was designed the robust PID controller satisfying disturbance attenuation and robust tracking performance using an H$\infty$ control method. The robust PID controller gains with the performances of the designed H$\infty$ controller are determined using the model matching method at frequency domain. Consequently, simulation results show that the proposed PID speed controller satisfies load torque disturbance attenuation and robust tracking performance, and this study has usefulness and applicability for the speed control system design of BLDC motors.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.46-51
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• 2004

The problem of reducing the effect of an unknown disturbance on a dynamical system is one of the most fundamental issues in control design. We propose a robust PID (Proportional Integral Derivative) control method with neural network for improving the performance due to the rejection of an unknown disturbance. The proposed system consists of a model of the plant, a conventional PID controller and a multi-layer neural network, and is composed of two loop; the first loop enables the system to achieve stability of system, the second loop rejects an unknown disturbance. Simulation and experiment results show that the proposed method improves considerably on the performance of the conventional PID control method and the typical IMC method using neural network.

• Korean Journal of Chemical Engineering
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• v.36 no.3
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• pp.356-367
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• 2019

This work presents an advanced and systematic approach to analytically design the optimal parameters of a two parameter second-order internal model control (IMC) filter that satisfies operational constraints on the output process, the manipulated variable as well as rate of change of the manipulated variable, for a first-order plus dead time (FOPDT) process. The IMC parameters are designed to minimize a control objective function composed of the weighted sum of the error between the process variable and the set point, and the rate of change of the manipulated variable, and to satisfy the desired constraints. The feasible region of the constrained IMC control parameters was graphically analyzed, as the process parameters and the constraints varied. The resulting constrained IMC control parameters were also used to find the corresponding industrial proportional-integral controller parameters of a Smith predictor structure.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.627-630
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• 2004

The PID type controller has been widely used in industrial application due to its simply control structure, ease of design, and inexpensive cost. However, control performance of the PID type controller suffers greatly from high uncertainty and nonlinearity of the system, large disturbances and so on. This paper presents a hybrid fuzzy logic proportional plus conventional integral derivative controller (fuzzy P+ID). In comparison with a conventional PID controller, only one additional parameter has to be adjusted to tune the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. Finally, the proposed hybrid fuzzy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controller is better than that of the conventional controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.346-349
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• 2003

For a traveling crane, various control methods such as neural network predictive control and TDOFPID(Two Degree of Freedom Proportional Integral Derivative) are studied. So in this paper, we proposed improved navigation method to reduce transfer time and sway with anti-collision path for avoiding collision in its movement to the finial coordinate. And we constructed the NNPPID(Neural Network Predictive PID) controller to control the precise move and speedy navigation. The proposed predictive control system is composed of the neural network predictor, TDOFPID controller, and neural network self-tuner. We analyzed ASC(Automated Stacking Crane) system and showed some computer simulations to prove excellence of the proposed controller than other conventional controllers.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.369-374
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• 2007

This paper presents yaw vibration control performances of railway vehicle featuring controllable magnetorheological damper. A cylindrical type of MR damper is devised and its damping force is evaluated by considering fluid resistance and MR effect. Design parameters are determined to achieve desired damping force level. The MR damper model is then incorporated with the governing equations of motion of the railway vehicle which includes vehicle body, bogie and wheel-set. Subsequently, computer simulation of vibration control via proportional-integral-derivative (PID) controller is performed using Matlab. Various control performances are demonstrated under external excitation by creep force between wheel and rail.