• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.40 no.4
• /
• pp.427-438
• /
• 1991

An iterative learning control scheme for exact-tracking control and parameter estimation of uncertain robotic system is preented. In the learning control structure, the control input converges globally and asymtotically to the desired input as iteration increases. Since convergence of parameter errors depends only on the persistent exciting condition of system trajectories along the iteration independently of the time-duration of trajectories, it may be achieved with system trajectories with small duration. In addition, the proposd learning control schemes are applicable to time-varying parametric systems as well as time-invariant systems, because the parameter estimation is performed at each fixed time along the iteration. In the parameter estimator, the acceleration information as well as the inversion of estimated inertia matrix are not used at all, which makes the proposed learning control schemes more feasible.

• Proceedings of the KIPE Conference
• /
• 2002.11a
• /
• pp.125-128
• /
• 2002

In this paper, a novel on-line do capacitance estimation method for the PWM converter is proposed. At no load, Input current at a low frequency is injected, which causes do voltage ripple. With the ac voltage and current ripple components of the dc side, the capacitance can be calculated. Experimental result shows that the estimation error is less than $2\%$.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.2 no.2
• /
• pp.242-250
• /
• 1999

This paper presents an estimation scheme of the external torque applied on the motor by using measured motor input current when the IPM(Interior Permanent Magnet) rotor type BLDC motor operates with constant speed. In general, the BLDC motor is controlled by vector control method. If it could be operated at over critical speed, the control scheme must be modified to flux-weakening control method. The external torque applied on the motor using flux-weakening control method could not be calculated by conventional torque equation because the demagnetizing current Id exists in the motor input current. In this paper, the commonly used flux-weakening control method is studied and the modified torque estimation scheme is suggested. The estimation scheme has been verified by the simulations and experimental results.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.16 no.4
• /
• pp.566-574
• /
• 2013

In this paper, direct power control system for three-phase PFC AC/DC converter without the source voltage sensors is proposed. The sinusoidal input current and unity effective power factor are realised based on the estimated flux in the observer. Both active and reactive power calculated using estimated flux. The estimation of flux is performed based on the reduced-order virtual flux observer using the actual currents and the command control voltage. Moreover, source voltage sensors are replaced by a estimated flux. DC output voltage has been compensated by DC output ripple voltage estimation algorithm. The active and reactive powers estimation are performed based on the estimated flux and Phase angle. The proposed algorithm is verified through simulation and experiment.

• Journal of Communications and Networks
• /
• v.16 no.4
• /
• pp.447-457
• /
• 2014

In this paper, we investigate the frequency domain channel estimation for multiple-input multiple-output (MIMO) single-carrier frequency-division multiple-access (SC-FDMA) systems. In MIMO SC-FDMA, code-division multiplexed (CDM) pilots such as cyclic-shifted Zadoff-Chu sequences have been adopted for channel estimation. However, most frequency domain channel estimation schemes were developed based on frequency-division multiplexing of pilots. We first develop a channel estimation error model by using CDM pilots, and then analyze the mean-square error (MSE) of various minimum MSE (MMSE) frequency domain channel estimation techniques. We show that the cascaded one-dimensional robust MMSE (C1D-RMMSE) technique is complexity-efficient, but it suffers from performance degradation due to the channel correlation mismatch when compared to the two-dimensional MMSE (2D-MMSE) technique. To improve the performance of C1D-RMMSE, we design a robust iterative channel estimation (RITCE) with a frequency replacement (FR) algorithm. After deriving the MSE of iterative channel estimation, we optimize the FR algorithm in terms of the MSE. Then, a low-complexity adaptation method is proposed for practical MIMO SC-FDMA systems, wherein FR is performed according to the reliability of the data estimates. Simulation results show that the proposed RITCE technique effectively improves the performance of C1D-RMMSE, thus providing a better performance-complexity tradeoff than 2D-MMSE.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06a
• /
• pp.873-878
• /
• 1994

Application of the filtered-x LMS adaptive filter to active noise cancellation requires to estimate the transfer characteristics between the output and the error signal of the adaptive canceller. We analyze the effects of estimation accuracy on the convergence behavior of the canceller when the input noise is modeled as a sinusoid.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.8
• /
• pp.3498-3511
• /
• 2016

We investigate the channel state information (CSI) in multi-input multi-output (MIMO) cooperative networks that employ the amplify-and-forward transmission scheme. Least squares and expectation conditional maximization have been proposed in the system. However, neither of these two approaches takes advantage of channel sparsity, and they cause estimation performance loss. Unlike linear channel estimation methods, several compressed channel estimation methods are proposed in this study to exploit the sparsity of the MIMO cooperative channels based on the theory of compressed sensing. First, the channel estimation problem is formulated as a compressed sensing problem by using sparse decomposition theory. Second, the lower bound is derived for the estimation, and the MIMO relay channel is reconstructed via compressive sampling matching pursuit algorithms. Finally, based on this model, we propose a novel algorithm so called sparsity adaptive expectation maximization (SAEM) by using Kalman filter and expectation maximization algorithm so that it can exploit channel sparsity alternatively and also track the true support set of time-varying channel. Kalman filter is used to provide soft information of transmitted signals to the EM-based algorithm. Various numerical simulation results indicate that the proposed sparse channel estimation technique outperforms the previous estimation schemes.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.6
• /
• pp.1055-1062
• /
• 2022

In order to use the Massive MIMO (Multi Input Multi Output) technology using the massive array antenna, it is essential to know the angle of arrival (AOA) of the signal. When using a massive array antenna, the existing AOA estimation algorithm has excellent estimation performance, but also has a disadvantage in that computational complexity increases in proportion to the number of antenna elements. To solve this problem, a cascade AOA estimation algorithm has been proposed and the performance of a single-shaped (non)massive array antenna has been proven through a number of papers. However, the computational complexity of the cascade AOA estimation algorithm to which single and double rim array antennas are applied has not been compared. In this paper, we compare and analyze the computational complexity for AOA estimation when single and double rim array antennas are applied to the cascade AOA estimation algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1432-1435
• /
• 1997

This paper proposes a fault detection and isolation metho by combining the parameter estimation method[4] with the observer method[2] to use merits of both methods. To verify the performance of the method proposed some simulations applied to remotely piloted vehicle are performed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.2538-2541
• /
• 2005

This paper deals one of the methods of system identification, especially on-line system identification in time-domain. The algorithm in this study needs all states of the system as well input to it for system identification. In this reason, Kalman filter is used for state estimation. But in order to implement a state estimator, the fact that a system model must be known is logical contradiction. To overcome this, state estimation and system parameter estimation are performed simultaneously in one sample. And the result of the system parameter estimation is used as basis to state estimation in next sample. On-line system identification comes, in every sample by performing both processes of state estimation and parameter estimation that are related mutually and recursively. This paper demonstrates the validity of proposed algorithm through an example of an unstable inverted pendulum system. This algorithm can be useful for on-line system identification of a system that has fewer number of measurable output than system order or number of states.