• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.556-561
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• 1987

This paper presents a suboptimal Kalman filter design method for the problem of tracking a maneuvering target. The design method is essentially based on linear target dynamics and linear-like structured measurements called pseudomeasurements. The pseudomeasurements are obtained by manipulating the original nonlinear measurements algebraically. The resulting filter has computational advantages over other filters with similar performance. Monte Carlo computer simulation results are included to demonstrate the effectiveness of the proposed suboptimal filter associated with the target acceleration model.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.456-465
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• 2006

The problem of recursive filtering far linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed algorithm can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filtering algorithm are demonstrated on the following examples: damper harmonic oscillator motion and vehicle motion constrained to a plane.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.163-170
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• 2003

The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.44-47
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• 1996

Bang-bang control law provides the optimal solution for a minimum-time control problem, but ignores the intermediate path except for the initial and final points. In this paper, a near minimum-time suboptimal fuzzy logic controller is introduced that can control the intermediate path. A dynamic model for a system is established using the average dynamics method of linearization. System model is continuously updated over the control time periods. This makes it suitable for high speed or variable payload applications. Bang-bang control theory is modified and used to derive the preliminary control law. A fuzzy logic algorithm is then applied to adjust and find the best solution. The solution will provide the suboptimal minimum-time control law which can avoid obstacles in the workspace.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.171-175
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• 1992

In this paper, we derive the optimal input-constrained control law which minimizes predictive control objective function subject to input constraints. In order to obtain the closed form of control law, three suboptimal methods are proposed and evaluated by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.747-752
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• 1987

The dynamic characteristics of a load-sensing hydraulic servo system are complex and highly unstable. Another property of the system is that the setting value of pump compensator is closely related to energy efficiency as well as control performance of the system. This necessitates the development of an effective control algorithm which guarantees good control performance, stability and energy efficiency. This paper considers a suboptimal PID control for the velocity control problem of the load-sensing hydraulic servo system. The results of simulations studies and experiments show that the proposed suboptimal controller can produce much better control performance than nonoptimal controllers and give effective energy efficiency.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.65-72
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• 1994

We resolve the suboptimal $\infty$ control problem using (J,J')-lossless coprime factorization by transforming the linear fractional transformation (LFT) into chain scattering description (CSD) in discrete-time systems. The condition transformed LFT into CSD is that the inverse matrix of $P_{21}$ of standard plant exists. But, this paper presents the method of transforming LFT into CSD for 4-block problem in case that the inverse matrix of $P_{21}$ of standard plant does not exist and parameterization of the all suboptimal $\infty$T controllers using (J,J')-lossless coprime factorization. It is shown that this method can resolve the suboptimal $\infty$ control problem solving only two Riccati equations in discrete-time systems.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.1006-1012
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• 2007

In this paper, a suboptimal homing guidance law for the homing missiles with an impact angle constraint is presented. Unlike general LQ optimal control, the aided loop ensuring some degrees of freedom for the constraint is introduced. Then an optimal feedback loop in consideration of the aided loop is designed by using Schwartz inequality. The aided loop is synthesized with the optimal control to produce the guidance command. Furthermore, to investigate the characteristics of the guidance law we carry out the comparative studies with other guidance laws. The results of the various computer simulations show the good performance of the proposed law.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.269-269
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• 2000

This paper presents a predictive control with H$_{\infty}$ suboptimal performance which is robust to disturbances and has a guaranteed stability. In order to derive the control law conveniently, state-space based approach, where the state variable is involved explicitly in the controller design and implementation is allowed. So an input-output model is converted to an equivalent observable canonical state-space form. The suggested control guarantees the norm bounded system output values from disturbances. A systematic way using the LMI method is presented to obtain appropriate parameters for Quadratic stability condition and optimization problem.