• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.577-580
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• 1996

LQ-servo is a stability-robustness guaranteed multivariable controller design method based on the LQR structure to improve command following performance with output feedback. In this paper, a new type of PI controller based on LQ-servo is introduced. Then, Command following performance is improved using the limiting behavior of the control gain and weighting factors on the low frequency part of design parameter Q that is the state weighting matrix in the cost function.

• Smart Structures and Systems
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• v.6 no.8
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• pp.921-938
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• 2010

Most of studies on control of beams containing piezoelectric sensors and actuators have been based on linear quadratic regulator (LQR) with state feedback or output feedback law. The aim of this study is to develop velocity-acceleration feedback law in the optimal control of smart piezoelectric beams. A new controller which is an optimal control system with velocity-acceleration feedback is presented. In finite element modeling of the beam, the variation of mechanical displacement through the thickness is modeled by a sinus model that ensures inter-laminar continuity of shear stress at the layer interfaces as well as the boundary conditions on the upper and lower surfaces of the beam. In addition to mechanical degrees of freedom, one electric potential degree of freedom is considered for each piezoelectric element layer. The efficiency of this control strategy is evaluated by applying to an aluminum cantilever beam under different loading conditions. Numerical simulations show that this new control scheme is almost as efficient as an optimal control system with state feedback. However, inclusion of the acceleration in the control algorithm increases practical value of a system due to easier and more accurate measurement of accelerations.

• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.6
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• pp.37-46
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• 2001

In this research, a controller design method based on optimization is proposed that can satisfy constraints on maximum responses of building structures subject to around excitation modeled by partially stochastic process. The class of controllers to be optimized is restricted to LQR. Weighting matrix on controlled outputs is used as design variable. Objective function, constraint functions and their gradients are computed by the parameterization of control gain with Riccati matrix. Full state feedback controllers designed by proposed optimization method satisfy various design objectives and their necessary maximum control forces are computed for the production of actuator. LQG controllers composed of Kalman filter and LQR designed by proposed method perform well with little deterioration. So it is possible to design output feedback controllers satisfying constraints on various maximum responses of structures.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.384-388
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• 1996

The closed-loop system modeling of an Active/semiactive suspension system has been accomplished through an artificial neural Networks. The 7DOF full model as the system equation of motion has been derived and the output feedback linear quadratic regulator has been designed for the control purpose. For the neural networks training set of a sample data has been obtained through the computer simulation. A 7DOF full model with LQR controller simulated under the several road conditions such as sinusoidal bumps and the rectangular bumps. A general multilayer perceptron neural network is used for the dynamic modeling and the target outputs are feedback to the input layer. The Backpropagation method is used as the training algorithm. The modeling of system and the model validation have been shown through computer simulations.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.421-423
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• 2007

In this paper, The authors apply a state feedback control using an optimal control theory to improve the stability of the control and the dynamic response of the DC-DC converter system with a number of different loads. To execute a this state feedback control, The authors present the pole placement technique using Linear Quadratic Regulator(LQR) to optimally control the system. An integrator can also be included in the open-loop path in order to minimize the steady-state error of the output voltage. To confirm the superiority of the controller, The simulation results are presented.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.593-609
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• 2004

Peak response is a more suitable index than mean response in the light of structural safety. In this study, a controller optimization method is proposed to restrict peak responses of building structures subject to earthquake excitations, which are modeled as partially stationary stochastic process. The constraints are given with specified failure probabilities of peak responses. LQR is chosen to assure stability in numerical process of optimization. Optimization problem is formulated with weightings on controlled outputs as design variables and gradients of objective and constraint functions are derived. Full state feedback controllers designed by the proposed method satisfy various design objectives and output feedback controllers using LQG also yield similar results without significant performance deterioration.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.7
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• pp.29-38
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• 1997

A closed-loop system modeling of an active/semiactive suspension system has been accomplished through an artificial neural network. A 7DOF full model as a system's equation of motion has been derived and an output feedback linear quadratic regulator has been designed for control purpose. A training set of a sample data has been obtained through a computer simulation. A 7DOF full model with LQR controller simulated under several road conditions such as sinusoidal bumps and rectangular bumps. A general multilayer perceptron neural network is used for dynamic modeling and target outputs are fedback to the a layer. A backpropagation method is used as a training algorithm. Model validation of new dataset have been shown through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.348-351
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• 1995

This note proposes a robust LQR method for systems with structured real parameter uncertainty based on Riccati equation approach. Emphasis is on the reduction of design conservatism in the sense of quadratic performance by utilizing the uncertainty structure. The class of uncertainty treated includes all the form of additive real parameter uncertainty, which has the multiple rank structure. To handle the structure of uncertainty, the scaling matrix with block diagonal structure is introduced. By changing the scaling matrix, all the possible set of uncertainty structures can be represented. Modified algebraic Riccati equation (MARE) is newly proposed to obtain a robust feedback control law, which makes the quadratic cost finite for an arbitrary scaling matrix. The remaining design freedom, that is, the scaling matrix is used for minimizing the upper bound of the quadratic cost for all possible set of uncertainties within the given bounds. A design example is shown to demonstrate the simplicity and the effectiveness of proposed method.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.161-169
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• 2007

The PLL equivalent augmented system incorporated with state feedback is proposed in this paper. The optimal value of filter time constant of loop filter in the phase-locked loop control system and the optimal state feedback gain designed by using linear quadratic regulator approach are derived. This approach allows the PLL control system to employ the large value of the phase-frequency gain $K_d$ and voltage control oscillator gain $K_o$. In designing, the structure of phase-locked loop control system will be rearranged to be a phase-locked loop equivalent augmented system by including the structure of loop filter into the process and by considering the voltage control oscillator as an additional integrator. The designed controller consisting of state feedback gain matrix K and integral gain $k_1$ is an optimal controller. The integral gain $k_1$ related to weighting matrices q and R will be an optimal value for assigning the filter time constant of loop filter. The experimental results in controlling the second-order lag pressure process using two types of loop filters show that the system response is fast without steady-state error, the output disturbance effect rejection is fast and the tracking to step changes is good.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1195-1199
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• 1998

In this paper a design method of robust power system stabilizers is proposed by means of robust linear quadratic regulator design technique under power system's operating condition change, which is caused by inner structure uncertainties and disturbances into a power system. It is assumed that the uncertainties present in the system are modeled as one equivalent signal. In this connections an optimal LQR control input for disturbance rejection, the output feedback gain for eliminating the disturbance are calculated. In this case. PSS input signal is obtained on the basis of weighted ${\Delta}P_e$ and $\Delta\omega$. In order to stabilize the overall control of system. Pole placement algorithm is applied in addition. making the poles of the closed loop system to move into a stable region in the complex plane. Some simulations have been conducted to verify the feasibility of the proposed control method on a machine to infinite bus power system. From the simulation results validation of the proposed method could be achieved by comparisons with the conventional PSS with phase lag-lead compensation.