• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.316-323
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• 1997

This paper compares input shaping techniques for controlling residual vibration of flexible structures. Input shaping generates vibration-reducing shaped commands through convolution of an impulse sequence with the desired command. Both feedforward and feedback control approaches with/without input shaper for uncertain dynamical systems are investigated to evaluate the control performances. The control objective is to achieve a fast settling time and robustness to plant uncertainty, to eliminate residual vibrations. It is shown by a series of simulation that a properly designed feedback controller with input shaper performs well, as compared with open-loop controller with input shaper.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.1
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• pp.61-66
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• 1997

로보트 매니퓰레이터가 일정한 접촉 힘을 유지하며 공작물의 표면을 따라가게 하는 작업은 많은 자동화 생산공정에서 유용하게 이용될 수 있다. 일반적인 위치제어용 산업용 로보트를 이러한 공정에 사용하기 위해서는 접촉힘이 계측되어 로보트의 제어에 이용되어야만 한다. 이 연구는 accommodation force control 방식으로 산업용 로보트를 제어하여 edge-following에 응용하도록하며, 접촉 힘의 계측에는 wrist force sensor를 사용한다. 이 시스템의 궤도추적속도와 force regulation 등이 예견제어에 의해 향상될 수 있다. 예견제어에 의해 설계된 전체 제어 시스템은 feedback 제어기와 feedforward 예견제어기로 구성된다. 여기서, 시스템의 안정성은 feedback 제어기에 의해서 결정되며, 예견제어기는 시스템에 미치는 외란을 통제하는 것을 주 기능으로 한다. 일반적으로 선형제어 방식을 채택한 경우와 예견제어를 이용한 edge-following을 실험을 통해 비교함으로써, 예견제어의 효용성을 확인한다.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1625-1636
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• 2017

A power electronic transformer (PET) based on the cascaded H-bridge (CHB) and the isolated bidirectional DC/DC converter (IBDC) is capable of accommodating a large scale battery energy storage system (BESS) in the medium-voltage grid, and is referred to as a power electronic transformer based battery energy storage system (PET-BESS). This paper investigates the PET-BESS and proposes a coordinative control strategy for it. In the proposed method, the CHB controls the power flow and the battery state-of-charge (SOC) balancing, while the IBDC maintains the dc-link voltages with feedforward implementation of the power reference and the switch status of the CHB. State-feedback and linear quadratic Riccati (LQR) methods have been adopted in the CHB to control the grid current, active power and reactive power. A hybrid PWM modulating method is utilized to achieve SOC balancing, where battery SOC sorting is involved. The feedforward path of the power reference and the CHB switch status substantially reduces the dc-link voltage fluctuations under dynamic power variations. The effectiveness of the proposed control has been verified both by simulation and experimental results. The performance of the PET-BESS under bidirectional power flow has been improved, and the battery SOC values have been adjusted to converge.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.12
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• pp.1295-1304
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• 2004

In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.931-942
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• 1999

This paper develops a 3 DOF vehicle model which includes lateral, roll and yaw motion to study a 4WS vehicle. The model is used for the simulation of a 4WS vehicle behavior, and to derive a control algorithm for rear wheel steering. This paper uses a feedforward plus feedback control scheme to compute a rear wheel steering angle. The feedforward control scheme for computing the first rear wheel steering angle uses a gain which is acquired by multiplying a proper value on a gain to maintain a zero sideslip angle. The feedback control scheme for computing the second rear wheel steering angle uses fuzzy logic and model following control scheme. A linear 2 DOF model is used as a reference model for model following control, and is derived from the developed 3 DOF model by neglecting sprung mass roll motion. A reference state variable is yaw rate, and is computed using the linear 2 DOF model. J-turn and lane change maneuver simulation are performed to show the effectiveness of the developed control scheme. The simulation results show that the 4WS vehicle with the developed control scheme has much better performance in yaw rate, lateral acceleration, roll angle, and sideslip angle than the 2WS vehicle. Also, the results show that the performance of the developed control is close to the one of an optimal control which assumes all states are perfect.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.285-302
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• 2010

Active aeroelastic control is an emerging technology aimed at providing solutions to structural systems that under the action of aerodynamic loads are prone to instability and catastrophic failures, and to oscillations that can yield structural failure by fatigue. The purpose of the aeroelastic control among others is to alleviate and even suppress the vibrations appearing in the flight vehicle subcritical flight regimes, to expand its flight envelope by increasing the flutter speed, and to enhance the post-flutter behavior usually characterized by the presence of limit cycle oscillations. Recently adaptive and robust control strategies have demonstrated their superiority to classical feedback strategies. This review paper discusses the latest development on the topic by the authors. First, the available control techniques with focus on adaptive control schemes are reviewed, then the attention is focused on the advanced single-input and multi-input multi-output adaptive feedback control strategies developed for lifting surfaces operating at subsonic and supersonic flight speeds. A number of concepts involving various adaptive control methodologies, as well as results obtained with such controls are presented. Emphasis is placed on theoretical and numerical results obtained with the various control strategies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.565-568
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• 2008

In this paper, we present bit error rate(BER) performance of an adaptive decision feedback equalizer(DFE) with experimental data. The experiment was performed at the shore of Geoje in November 2007. The BER performance of the adaptive DFE whose tap weight is updated by RLS is described with change of feedforward tap number, feedback tap number, traning seqence length and delay, which shows that the uncoded average BER is $4{\times}10^{-2}\;and\;1.5{\times}10^{-2}$ with transmission range 9.7km and 4km, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.321-324
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• 1997

In this paper, Active Noise Control(ANC) algorithm is proposed based on the estimated frequency estimator of the reference signal. The conventional feedforward ANC algorithms should measure the reference and use it to calculate the gradient of the squared error and filter coefficients. For ANC systems applied to aircrafts and passenger ships, engines from which reference signal is usually measured is so far from seats where main part of controller is placed that the scheme might be difficult to implement or very costly. Feedback ANC algorithm which doesn't need to measure the reference uses the error signal to update the filter and is sensitive to unexpected transient noise like a sneeze, clapping of hands and so on The proposed algorithm estimates frequencies of the desired signal in real time using adaptive notch filter. New frequency estimation algorithm is proposed with the improved convergence rate, threshold SNR and computational simplicity. Reference is not measured but created with the estimated frequencies. It has strong similarity to the conventional feedback control because reference is made from error signal. Enhanced error signal is used to update the controller for better performance under the measurement noise and impact noise. The proposed ANC algorithm is compared with the conventional feedback control.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.214-221
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• 1998

A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.171-179
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• 2004

The purpose of this study is to generate cycling motion for FES (functional electrical stimulation) using knee muscles only. We investigated the possibility by simulation. The musculoskeletal model used in this simulation was simplified as 5-rigid links and 2 muscles (knee extensor and flexor). For the improvement of the present feedforward control in FES, we included feedback path in the control system. The control system was developed based on the biological neuronal system and was represented by three sub-systems. The first is a higher neuronal system that generates the motion command for each joint. The second is the lower neuronal system that divides the motion command to each muscle. And the third is a sensory feedback system corresponding to the somatic sensory system. Control system parameters were adjusted by a genetic algorithm (GA) based on the natural selection theory. GA searched the better parameters in terms of the cost function where the energy consumption, muscle force smoothness, and the cycling speed of each parameter set (individual) are evaluated. As a result, cycling was implemented using knee muscles only. The proposed control system based on the nervous system model worked well even with disturbances.