• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.8-13
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• 1998

A tracking controller is presented for RLFJ(rigid link flexible joint) robot manipulators with only position measurements. The controller is developed based on the integrator backstepping design method and on the two observers: the first is simple linear form observer for the filtered link velocity errors and the other for the actuator velocities. The proposed controller achieves exponential tracking of link positions and velocities while keeping all internal signals bounded. It also guarantees exponential convergence of the estimated signals to their actual ones. Finally, simulation results are included to demonstrate the tracking performance.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.78-85
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• 1996

The set of admissible time-variations in the input signal can be separated into two categories : slow parameter changes and large parameter changes which occur infrequently. A common approach used in the tracking of slowly time-varying parameters is the exponential recursive least-squares(RLS) algorithm. There have been a variety of research works on the error analysis of the exponential RLS algorithm for the slowly time-varying parameters. In this paper, the focus has been given to the error analysis of exponential RLS algorithms for the input data with abrupt property changes. The voiced speech signal is chosen as the principal application. In order to analyze the error performance of the exponential RLS algorithm, deterministic properties of the exponential RLS algorithms is first analyzed for the case of abrupt parameter changes, the impulsive input(or error variance) synchronous to the abrupt change of parameter vectors actually enhances the convergence of the exponential RLS algorithm. The analysis has also been verified through simulations on the synthetic speech signal.

• Journal of Power Electronics
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• v.19 no.2
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• pp.549-559
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• 2019

This paper proposes an adaptive sliding mode control (ASMC) strategy with an enhanced optimal reaching law (EORL) for the robust current tracking control of the boost converter based hybrid power source (HPS) in an electric vehicle (EV). A conventional ASMC strategy based on state observers and the hysteresis control method is used to realize the current tracking control for the boost converter based HPS. Then a novel enhanced exponential reaching law is proposed to improve the ASMC. Moreover, an enhanced exponential reaching law is optimized by particle swarm optimization. Finally, the adaptive control factor is redesigned based on the EORL. Simulations and experiments are established to validate the ASMC strategy with the EORL. Results show that the ASMC strategy with the EORL has an excellent current tracking control effect for the boost converter based HPS. When compared with the conventional ASMC strategy, the convergence time of the ASMC strategy with the EORL can be effectively improved. In EV applications, the ASMC strategy with the EORL can achieve robust current tracking control of the boost converter based HPS. It can guarantee the active and stable power distribution for boost converter based HPS.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.13 no.6
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• pp.284-289
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• 2003

The electric furnace, inside which the desired temperature is kept by the generated heat, is known to be a difficult system to control and model exactly because system parameters and response delayed time are varied as the temperature and positions are changed. In this study, the GPCEW (generalized predictive control with exponential weight), which always guarantees the stability of the closed loop system and can be effectively applied to the internally unstable system, was introduced to the ceramic drying electric furnace and was verified by showing its temperature tracking performance experimentally.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.330-336
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• 2005

An intelligent control method is proposed for control of rigid robot manipulators which achieves exponential tracking of repetitive robot trajectory under uncertain operating conditions such as parameter uncertainty and unknown deterministic disturbance. In the learning controller, exponentially stable learning algorithms are combined with stabilizing computed error feedforward and feedback inputs. It is shown that all the error signals in the learning system are bounded and the repetitive robot motion converges to the desired one exponentially fast with guaranteed convergence rate. An engineering workstation based control system is built to verify the effectiveness of the proposed control scheme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.331-335
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• 2001

In this paper, we propose two novel blind LMS algorithms, called exponential step sire LMS algorithms (ES-LMS), for adaptive array antennas whose convergence speed is increased, hence they are much more capable of tracking the desired signal than the conventional LMS algorithms. Both of the algorithms require neither spatial knowledge nor reference signals since they use the finite symbol property of digital signal. Computer simulations were carried cot in CDMA environment affected by multi-path Rayleigh fading to verify the performance of the two proposed algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.167.2-167
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• 2001

This paper presents guidance and control laws which guarantee a minimum fuel consumption and have obustness against various disturbances during a terminal-landing phase on the lunar surface. The nonlinear robust tracking control system is designed to track the reference profiles, which are expressed by exponential functions. An adjustment law in the tracking controller is given in the form of the differential equations with respect to the controller´s variable gains. Computer simulations are performed to examine the tracking accuracy, the robustness in a thrust failure mode, and the vertical soft landing at a pre-assigned point on the lunar surface. The results of numerical simulation show the effectiveness of the present control law.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1201-1209
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• 1991

Generally, the conventional tracking algorithms are very limited in the practical applications because of its exponential increase in the required computation time for the number of targets being tracked. Therefore, in this paper, a new real-time moving target tracking system is proposed, which is based on the neural networks with massive parallel processing capabilities. Through the theoretical and experimental results, the target tracking system based on neural network algorithm is analyzed to be computationally independent of the number of objects being tracked and performs the optimized tracking through its massive parallel computation and learning capabilities. And this system also has massive matched filtering effects because the moving target data can be compactly stored in the interconnection weights by learning. Accordingly, a possibility of the proposed neural network target tracking system can be suggested to the fields of real-time application.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.257-264
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• 2015

For the tracking of the way-points of hovering AUV (HAUV), we suggest a simple PID controller. The way-points are designed to approach to a virtual underwater structure and the heading angles at each way-point are set to look at the structure in the face. The proposed controller consists of a vertical controller to maintain the depth and pitch angle, and a horizontal controller to move to the desired position as well as to adjust the heading angle of the HAUV. In the simulation using Matlab/Simulink, the HAUV with the proposed PID controller is shown to track all the way-points within 1 m range while maintaining proper heading angle at each way-point.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.235-243
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• 2000

The probabilistic data association filter(PDAF) is known to provide better tracking performance than the standard Kalman filter(KF) in a cluttered environment. In this paper, the stability of the PDAF of Fortmann et al[7], in the presence of uncertainties with regard to the origin of measurement, is investigated. The modified Riccati equation derived by approximating two random terms with their expectations is used to prove the stability of the PDAF. A new Lyapunov function based approach, which is different from the quantitative evaluation of Li and Bar-Shalom[7], is pursued. With the assumption that the system and observation noises are bounded, specific tracking error bounds are established.