• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.337-341
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• 2003

This paper provides a design method for a dynamic output feedback controller which stabilizes a class of linear time invariant systems. We suppose all the states of the given system is not measurable and only the outputs are used to stabilize the system. The systems considered cannot be stabilized by a static output feedback only. In the scheme we first assume that the given system can be stabilized by a state feedback composed of its output, velocity of the output and its higher order derivative terms. Instead of using the derivatives of the output, however, a dynamic system is constructed systematically which replaces the role of the derivative terms. Then, a high-gain output feedback stabilizes the composite system together with the newly constructed system. The performance of the proposed control law is illustrated in the comparative simulation studies of a numerical example with an observer-based control law.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.2-75
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• 2001

In this paper, we provide the technique of H$\infty$ controller design algorithm for input-saturated linear systems using a linear parameter varying(LPV) framework. The LPV controller with parameter dependent dynamic state feedback controller concept guarantees the asymtotic stability and H$\infty$ norm bound within prescribed level v using the saturation nonlinearity as scheduling parameters. Especially, the sufficient conditions for the existence of H$\infty$ controller are formulated in terms of linear matrix inequalities(LMIs) that can be solved very efficiently.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.126-126
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• 2000

In this paper, we provide an approach of controller synthesis for input-saturated linear systems by a linear parameter varying (LPV) framework. Using directly the saturation nonlinearity as scheduling parameters, we propose an LPV-stabilizer with parameter-dependent dynamic state-feedback controller concept. Especially, the synthesis conditions are formulated in terms of linear matrix inequalities (LMIs) that can be solved very efficiency.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.278-281
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• 2008

Linear motor (LM) based shuttle cars will play an important role in the future transportation systems of marine terminals to cope with increasing container flows. These systems are known as agile port terminals because of their significant advantages. However, routing for multiple shuttle cars is still an open issue. We present a network model of a container yard and propose constrained dynamic programming (DP) for its routing strategy with collision avoidance. The algorithm is a modified version of typical DP which is used to find an optimal path for a single traveler. We evaluate the new algorithm through simulation results for three shuttle cars in a mesh-type container yard.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.280-285
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• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• Journal of Mechanical Science and Technology
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• v.14 no.10
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• pp.1051-1060
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• 2000

Linear cyclic systems (LCS's) are a class of systems whose dynamic behavior changes cyclically. Such cyclic behavior is ubiquitous in systems with fundamentally repetitive motions (e. g. all rotating machinery). Yet, the knowledge of the noise and vibration transmission paths in LCS's is quite limited due to the time-varying nature of their dynamics. The first part of this two-part paper derives a generic expression that describes how the noise and/or vibration are transmitted between two (or multiple) locations in the LCS's. An analysis via the Fourier series and Fourier transform (FT) plays a major role in deriving this expression that turns out to be transfer function dependent upon the cycle position of the system. The cyclic nature of the LCS' transfer functions is shown to generate a series of amplitude modulated input signals whose carrier frequencies are harmonic multiples of the LCS' fundamental frequency. Applicability of signal processing techniques used in the linear time-invariant systems (LTIS's to the general LCSs is also discussed. Then, a criterion is proposed to determine how well a LCS can be approximated as a LTIS. In Part II, experimental validation of the analyses carried out in Part I is provided.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.121-128
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• 2000

The scope of this paper is focused to the systems which have the time period and they should be necessarily studied in the sense of stability and design method of controller to stabilize the orignal unstable systems. In general, the time periodic systems or the systems having same motions during certain time interval are easily found in rotating motion device, i.e., satellite or helicopter and widely used in factory automation systems. The characteristics of the selected dynamic systems are analyzed with the new stability concept and stabilization control method based on Lyapunov direct method. The new method from Lyapunov stability criteria which satisfies the energy convergence is studied with linear algebraic method. And the numerical procedures are developed with computational programming method to apply to the practical linear periodic systems. The results from this paper demonstrate the usefulness in analysis of the asymptotic stability and stabilization of the unstable linear periodic system by using the developed simulation procedures.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.445-453
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• 2007

This paper presents a robust controller design approach for improving vehicle dynamic roll motion performance and guaranteeing the closed-loop system stability in spite of vehicle parameter variations resulting from aging elements, loading patterns, and driving conditions, etc. The designed controller is linear parameter-varying (LPV) in terms of the time-varying parameters; its control objective is to minimise the $H_{\infty}$ performance from the steering input to the roll angle while satisfying the closed-loop pole placement constraint such that the optimal dynamic roll motion performance is achieved and robust stability is guaranteed. The sufficient conditions for designing such a controller are given as a finite number of linear matrix inequalities (LMIs). Numerical simulation using the three-degree-of-freedom (3-DOF) yaw-roll vehicle model is presented. It shows that the designed controller can effectively improve the vehicle dynamic roll angle response during J-turn or fishhook maneuver when the vehicle's forward velocity and the roll stiffness are varied significantly.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.213-215
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• 2007

This paper introduces an emotional behavior decision model for intelligent service robots. An emotional model should make different behavior decisions according to the purpose of the robots. We propose an emotional behavior decision model which can change the character of emotional model and make different behavior decisions although the situation and environment remain the same. We defined each emotional element such as reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics by state dynamic equations. The proposed system model is a linear system. If you want to add one external stimulus or behavior, you need to add just one dimensional vector to the matrix of external stimulus or behavior dynamics. The case of removing is same. The change of reactive dynamics, internal dynamics, emotional dynamics, and behavior dynamics also follows the same procedure. We implemented the proposed emotional behavior decision model and verified its performance.