• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.355-361
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• 1992

In this paper, some rules for auto- tuning of feedback-feedforward controller in variable load and disturbance are presented. The parameters of feedback PID controller are determined by heuristic rules based on input regulation experiment, and the parameters of feedforward controller are determined by result rules based on spectral factorization, minimum variance, and polynomial equation. These heuristic and result rules are used as an element of the feedback loop in an auto-tuning feedback-feedforward controller. The robust and accurate control performance is demonstrated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.590-596
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• 2004

A passivity-based dynamic output feedback controller design is considered for a finite collection of non-square linear systems. Design of a single controller for a set of plants i.e. simultaneous stabilization is an important issue in the area of robust control design. We first determine a squaring gain matrix and an additional dynamics that is connected to the systems in a feedforward way, then a static passivating control law is designed. Consequently, the actual feedback controller will be the static control law combined with the feedforward dynamics. A necessary and sufficient condition for the existence of the parallel feedforward compensator is given by the static output feedback formulation. In contrast to the previous result [1], a technical condition for constructing the parallel feedforward compensator is removed by proposing a new type of the parallel compensator.

• Journal of KSNVE
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• v.4 no.4
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• pp.497-505
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• 1994

This paper presents a design scheme of the active noise absorber that consists of the feedforward and feedback controller. The feedback controller aims to increase damping for the specific acoustic mode. The feedforward controller synthesizes the input signal coherent with the primary noise source in order to attenuate the noise field in the broad frequency range. The feedforward controller is adapted to the variation of acoustic plants using the proposed algorithm which compensates the effect of feedback link. Experimental results demonstrate that the proposed method is effective for the active control of band-limited noise fields in the enclosure.

• Journal of KSNVE
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• v.9 no.1
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• pp.33-41
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• 1999

This paper presents a method of actively controlling the interior noise by a trim panel with hybrid feedforward-feedback control loop. The control technique is designed to minimize the vibration of panel whose motion is limited to that of a piston (out-of-plane motion). The hybrid controller consists of an adaptive feedforward controller in conjunction with a linear quadratic Gaussian (LQG) feedback controller. In order to maintain control performance of both persistent and transient disturbances, the feedback loop speeds up the adaptation rate of feedforward controller by improving damping capacity of secondary plant related with the adaptation rule. Numerical simulation and experimental result indicate that the hybrid controller is a more effective method for reducing the vibration of the panel (and therefore the interior noise) compared to using feedforward controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.349-352
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• 1997

A Piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearity is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearity can not be neglected. The hysteresis nonlinearity of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearity problem. And feedforward-feedforward-feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, and PID control is sued as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded hat the proposed control scheme gives good tracking performance.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.200-209
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• 1999

This paper presents a precise tracking control scheme for the proposed parallel robot using artificial neural network. This control scheme is composed of three feedback controllers and one feedforward controller. Conventional PD controller and artificial neural network are used as feedback and feedforward controller respectively. A backpropagation learning strategy is applied to the training of artificial neural network, and PD controller outputs are used as target outputs. The PD controllers are designed at the robot dynamics based on inter-relationship between active joints and moving platform. Feedback controllers insure the total stability of system, and feedforward controller generates the control signal for trajectory tracking. The precise tracking performance of proposed control scheme is proved by computer simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.288-293
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• 2009

This study presents the active noise control of duct noise. The duct was excited by a steady-state harmonic and white noise force and the control was performed by one control speaker attached to surface of the duct. An adaptive controller based on filtered x LMS(FXLMS) algorithm was used and controller was defined by minimizing the square of the response of the error microphone. The assemble controller, which is called a hybrid ANC(active noise control) system, was combined with feedforward and feedback controller. The feedforward ANC attenuates primary noise that is correlated with the reference signal, while the feedback ANC cancels the narrowband components of the primary noise that are not observed by the reference sensor. Furthermore, in many ANC applications, the periodic components of noise are the most intense and the feedback ANC system has the effect of reducing the spectral peaks of the primary noise, thus easing the burden of the feedforward ANC filter.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.2
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• pp.140-148
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• 1996

This paper proposes a combined feedforward/feedback controller which uses jacobians of neural network. The jacobians are calculated form the neural network that identifies the nonlinear plant, which are used for designing a jacobian controller and for training a neural network controller. Normally, it takes much time to train the neural network controller. Combining the neural and the jacobian controller, it can be a stable controller from the beginning of training phase of neural network, and it can be implemented as a learning-while-functioning controller. Simulated resutls for the proposed controller show its effectiveness and better performances.