• 대한기계학회논문집
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• 제16권7호
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• pp.1307-1313
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• 1992

본 연구에서는 유연한 조작기의 끝점 위치 제어를 위해 LQG/LTR 제어법을 적 용함으로서 기존의 고전적 제어방법과 비교하고자 한다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 춘계학술대회논문집
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• pp.192-195
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• 2005

In this study, dynamic characteristics of an end-capped hull structure with surface bonded piezoelectric actuators are studied. Finite element modeling is used to obtain practical governing equation of motion and boundary conditions of smart hull structure. Modal analysis is conducted to investigate the dynamic characteristics of the hull structure. Piezoelectric actuators are attached where the maximum control performance can be obtained. Active controller based on Linear Quadratic Gaussian (LQG) theory is designed to suppress vibration of smart hull structure. It is observed that closed loop damping can be improved with suitable weighting factors in the developed LQG controller.

• 소음진동
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• 제7권1호
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• pp.161-168
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• 1997

The present paper describes the vibration control experiment of composite plates with bonded piezoelectric sensor and actuator. The system is modeled as two degree-of-freedom system using modal coordinates and the system parameters are obtained from vibration tests. Kalman filter is adopted for extracting modal coordinates from sensor signal, and control algorithms applied to the system are Linear Quadratic Gaussian(LQG) control, Bang-Bang Control (BBC), Negative Velocity Feedback(NVF), Proportional Derivative Control(PDC). From observation of the spillover and control perfomance, it is concluded that a higher order control algorithm such as LQG rather than BBG, NVF, PDC is suitable for efficient simultaneous control of both bending and twisting modes of composite plates.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.266-268
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• 2006

In this paper, we design a Linear Quadratic Gaussian(LQG) controller for active suspensions. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Using an optimization-algorithm, Evolution Strategy(ES), we find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.552-558
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• 1993

In this paper, control of a planar two-link structurally flexible robotic manipulator executing unconstrained and constrained maneuvers is considered. The dynamic model, which is obtained by using the extended Hamilton's principle and the Galerkin criterion, includes the impact force generated during the transition from unconstrained to constrained segment of the robotic task. A method is presented to obtain the linearized equations of motion in Cartesian space for use in designing the control system. The linear quadratic Gaussian with loop transfer recovery (LQG/LTR) design methodology is exploited to design a robust feedback control system that can handle modeling errors and sensor noise, and operate on Cartesian space trajectory errors. The LQG/LTR compensator together with a feedforward loop is used to control the flexible manipulator. Simulated results are presented for a numerical example.

• International Journal of Control, Automation, and Systems
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• 제4권1호
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• pp.1-9
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• 2006

This paper presents the modeling and multivariable feedback control of a novel high-precision multi-dimensional positioning stage. This integrated 6-degree-of-freedom. (DOF) motion stage is levitated by three aerostatic bearings and actuated by 3 three-phase synchronous permanent-magnet planar motors (SPMPMs). It can generate all 6-DOF motions with only a single moving part. With the DQ decomposition theory, this positioning stage is modeled as a multi-input multi-output (MIMO) electromechanical system with six inputs (currents) and six outputs (displacements). To achieve high-precision positioning capability, discrete-time integrator-augmented linear-quadratic-regulator (LQR) and reduced-order linearquadratic-Gaussian (LQG) control methodologies are applied. Digital multivariable controllers are designed and implemented on the positioning system, and experimental results are also presented in this paper to demonstrate the stage's dynamic performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1530-1533
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• 2005

In this paper, we design a Linear Quadratic Gaussian controller for the active suspension. We can improve the inherent suspension problem, trade-off between the ride quality and the suspension travel by selecting appropriate weights in the LQ-objective function. Because any definite rules for selecting weights do not exist, we use an optimization-algorithm, Evolution Strategy (ES) to find the proper control gains for selected frequencies, which have major effects on the vibrations of the vehicle's state variables. The frequencies and proper control gains are used for the neural network data. During a vehicle running, the trained on-line neural network is activated and provides the proper gains for non-trained frequencies. For the full-state feedback control, Kalman filter observes the full states and Fourier transform is used to detect the frequency of the road.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.131-138
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• 1999

In discrete-time controlled system, sampling time is one of the critical parameters for control performance. It is useful to employ different sampling rates into the system considering the feasibility of measuring system or actuating system. The systems with the different sampling rates in their input and output channels are named multirate system. Even though the original continuous-time system is time-invariant, it is realized as time-varying state equation depending on multirate sampling mechanism. By means of the augmentation of the inputs and the outputs over one Period, the time-varying system equation can be constructed into the time-invariant equation. In this paper, an alternative time-invariant model is proposed, the design method and the stability of the LQG (Linear Quadratic Gaussian) control scheme for the realization are presented. The realization is flexible to construct to the sampling rate variations, the closed-loop system is shown to be asymptotically stable even in the inter-sampling intervals and it has smaller computation in on-line control loop than the previous time-invariant realizations.

• 대한기계학회논문집
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• 제18권6호
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• pp.1399-1411
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• 1994

A model helicopter has more versatile flight capability than the fixed-wing aircraft and it can be used as an unmaned vehicle in hazardous area. A helicopter, similar to other aircrafts, is an unstable, multi-input multi-output nonlinear system exposed to strong disturbance. So it should be controlled by robust control theories that can be applied to multivariable systems. In this study, motion equations of hovering are established, linearized and transformed into a state equation form. Various parameters are measured and calculated in other to obtain the stability derivatives in the state equation. Hovering flight controller is designed using the digital LQG/LTR(Linear Quadratic Gaussian/Loop Transfer Recovery) control theory. The designed controller is tested by the nonlinear simulations and implemented on an IBM-PC/386. Experiments were carried out on a model helicopter attached to the 3-DOF gimbal. The designed controller showed satisfactory hovering capability to maintain the hovering for more than 40 seconds.

• 한국기계가공학회지
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• 제14권3호
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• pp.57-64
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• 2015

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.