• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1244-1254
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• 2015

This paper proposes a new method for tuning a linear quadratic - proportional integral derivative controller for second order systems to simultaneously meet the time and frequency domain design specifications. The suitable loop-shape of the controlled system and the desired step response are considered as specifications in the time and frequency domains, respectively. The weighting factors, Q and R of the LQ controller are determined by the algebraic Riccati equation with respect to the limiting behavior and target function matching. Numerical examples show the effectiveness of the proposed LQ-PID tuning method

• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.473-480
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• 2005

A three axes marine satellite antenna has been developed. As a design step, a CAD model for the antenna has been created according to the design requirements. Kinematic analyses are carried out to insure design specification and to check collision detection of the CAD model. Marine satellite antennas experience base motions, and a relevant control system should control the three antenna axis to point to the satellites accurately. A sensor fusion algorithm and a PIDA (Proportional, Integral, Derivative, Acceleration) control algorithm are designed and implemented to control the yaw, level, and cross-level angle of a small size satellite marine antenna. Antenna stabilization control experiments are performed using a test simulator which gives the antenna base motions. Experimental results show small pointing errors, which is less than 0.2 degree for the level, cross-level, and yaw axis.

• Journal of Power Electronics
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• v.16 no.2
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• pp.564-571
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• 2016

This study proposes an adaptive neuro-fuzzy inference system (ANFIS)-based rotor position controller for brushless direct current (BLDC) motors to improve the control performance of the drive under transient and steady-state conditions. The dynamic response of a BLDC motor to the proposed ANFIS controller is considered as standard reference input. The effectiveness of the proposed controller is compared with that of the proportional integral derivative (PID) controller and fuzzy PID controller. The proposed controller solves the problem of nonlinearities and uncertainties caused by the reference input changes of BLDC motors and guarantees a fast and accurate dynamic response with an outstanding steady-state performance. Furthermore, the ANFIS controller provides low torque ripples and high starting torque. The detailed study includes a MATLAB-based simulation and an experimental prototype to illustrate the feasibility of the proposed topology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.177-181
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• 2003

PID(Proportional-Integral-Derivative)제어기는 제어악고리즘의 단순성과 실 현장에서의 강인성 등으로 산업용 보일러의 제어시스템서 주로 이용되어 왔다. 그러나 다중 루프를 가진 보일러 시스템에서는 루프간의 상호 간섭 영향 등으로 부하 변화에 따라 요구되는 증기(steam)압력, 증기 유량(steam flow)변화 등을 동시에 만족하도록 급수 유량, 연교 유량, 공기 유량 등을 PlH제어기만으로 제어하는 것은 어렵다. 본 연구에서는 보일러 시스템의 다목적 성능 최적화에 각각의 적합도 함수 $f_{a}$ , $f_{b}$, $f_{c}$를 정의하고 면역 알고리즘을 이용해 최적화를 구하고 그 결과에 대한 특성과 유효성을 검토하였다.다.다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.86-86
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• 2000

This paper describes a neural network based fuzzy type PID control scheme. The PID controller is being widely used in industrial applications. however, it is difficult to determine the appropriate PID gains for (he nonlinear system control. In this paper, we re-analyzed the fuzzy controller as conventional PID controller structure, and proposed a neural network based fuzzy type PID controller whose scaling factors were adjusted automatically. The value of initial scaling factors of the proposed controller were determined on the basis of the conventional PID controller parameters tuning methods and then they were adjusted by using neural network control techniques. Proposed controller was simple in structure and computational burden was small so that on-line adaptation was easy to apply to. The result of practical experiment on the magnetic levitation system, which is known to be hard nonlinear, showed the proposed controller's excellent performance.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2161-2163
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• 2002

The PID type controller has been widely used in industrial application doc 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 Fuazy P+ID controller is applied to BLDC motor drive. Simulation results demonstrated that the control performance of the proposed controlled is better than that of the conventional controller.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.134-139
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• 2007

A delivery ship is used to handle the cargo with the crane to/from the ships. The ship is inclined in the direction of a cargo which is hung on a crane. In this case, a arc shaped rail should be in the equilibrium state to get good anti-rolling performance. In this study, a device and control algorithm are developed to take accurate and quick equilibrium of the rail. The device is composed of a hinged immovable support, screw jack and damper. And the control system is based on I-PD control law to consider of control input saturation and overshoot. The controller is composed of integral controller of feedforward path and proportional-derivative controller of feedback path. The parameters of controller is designed to follow the reference signal and to remove overshoot. The simulation results show that the desirable control performance is achieved.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.480-487
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• 1999

In this paper we propose a neural network precompensated PID(NNP PID) controller for load frequency control of 2-area power system. While proportional integral derivative(PID) controllers are used in power system they have many problems because of high nonlinearities of the power system So a neural network-based precompensation scheme is adopted into a conventional PID controller to obtain a robust control to the nonlinearities. The applied neural network precompen-sator uses an error back-propagation learning algorithm having error and change of error as inputand considers the changing component of forward term of weighting factor for reducing of learning time. Simulation results show that the proposed control technique is superior to a conventional PID controller and an optimal controller in dynamic responses about load disturbances. The pro-posed technique can be easily implemented by adding a neural network precompensator to an existing PID controller.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.194-203
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• 2008

Iterative learning control (ILC) is a simple and effective method for the control of systems that perform the same task repetitively. ILC algorithm uses the repetitiveness of the task to track the desired trajectory. In this paper, we propose a PID (proportional plus integral and derivative) type ILC update law for control discrete-time single input single-output (SISO) linear time-invariant (LTI) systems, performing repetitive tasks. In this approach, the input of controlled system in current cycle is modified by applying the PID strategy on the error achieved between the system output and the desired trajectory in a last previous iteration. The convergence of the presented scheme is analyzed and its convergence condition is obtained in terms of the PID coefficients. An optimal design method is proposed to determine the PID coefficients. It is also shown that under some given conditions, this optimal iterative learning controller can guarantee the monotonic convergence. An illustrative example is given to demonstrate the effectiveness of the proposed technique.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.69.1-69.1
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• 2013

We are developing Adaptive Optics (AO) system for 1m telescope at Gwacheon National Science Museum Observatory. The beam spot of the Gwacheon 1m telescope. The tip-tilt correction system consists of a CMOS sensor, a tip-tilt mirror and a feed back loop. The beam spot location at the CMOS sensor indicates the tip-tilt components of the incoming light. The tip-tilt mirror is controlled by DAC output voltage calculated by proportional-integral-derivative (PID) controller. This system successfully corrects the tip-tilt motion of the spot.