• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.594-596
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• 2005

This paper present a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation and experimental results with 24 DOF biped robot.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.12
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• pp.991-997
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• 2002

The concept of feasible & invariant region plays an important role to derive closed loop stability and achie adequate performance of constrained receding horizon predictive control. In this paper, we define a complex polyhedral feasible & invariant set for all stabilizable input-constrained linear systems by using a complex transform and propose a one-norm based receding horizon control scheme using these invariant sets. In order to get a larger stabilizable set, a convex hull of invariant sets which are defined for different state feedback gains is used as a target invariant set of the constrained receding horizon control. The proposed constrained receding horizon control scheme is formulated so that it can be solved via linear programming.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.6
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• pp.830-836
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• 2012

Various control algorithms have been proposed for the speed-sensorless control for an induction motor. These control schemes are mainly based on the speed feedback with the flux and speed estimations. This paper proposes another method for the speed-sensorless control for an induction motor. The proposed scheme is based on the torque and flux compensation without speed estimations, in which the same controlled stator voltage is applied to both the induction motor and the numerical model so that the differences between torques and fluxes of the model and the induction motor may be compelled to give access to zero. The results of experiment show the effectiveness of the scheme.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.6
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• pp.731-737
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• 1999

This paper describes the path tracking control for a mobile robot which has two casters at the front and rear to keep balance and two driving wheels on the left and right sides of its body. Power wheeled steering method is adapted to control heading of the robot. It is very difficult to find appropriate feedback gains when linear regulator control scheme is adapted to path tracking con-trol of this type of robot. Therefore in this paper we propose the path tracking control algorithm using the fuzzy logic control scheme for this type of root. Simulation to prove the validity of the proposed two algorithms is performed. The results are reported as last part in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.396-400
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• 1996

It is prerequisite to apply dynamics controller to control robot manipulator required to perform fast and Precise motion. In this Paper, we Propose an adaptive 3earning control method for the dynamic control of a robot manipulator. The proposed control scheme is made up of PD controller in the feedback loop and the adaptive learning controller in the feedforward loop. This control scheme has the ability to estimate uncertain dynamic parameters included intrinsically in the system and to achieve the desired performance without the nasty matrix operation. The proposed method is applied to a SCARA robot and experimentally verified.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.6B
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• pp.495-507
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• 2006

In wireless local area networks (WLANs), the successful design of scheduling algorithm is a key factor in guaranteeing the various quality of service (QoS) requirements for the stringent real-time constraints of multimedia services. In this paper we propose a multipolling-based dynamic scheduling algorithm for providing delay guarantees to multimedia traffics such as MPEG streams. The dynamic algorithm exploits the characteristics of MPEG stream, and uses mini frames for feedback control in order to deliver dynamic parameters for channel requests from stations to the point coordinator (PC) operating at the access point (AP). In this scheme, the duration of channel time allocated to a station during a superframe is changed dynamically depending on the MPEG frame type, traffic load and delay bound of the frame, etc. Performance of the proposed scheme is investigated by simulation. Our results show that compared to conventional scheme, the proposed scheme is very effective and has high performance while guaranteeing the delay bound.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.68-74
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• 2008

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.47-50
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• 2003

For large-scale systems which are composed of interconnections of many lower-dimensional subsystems, decentralized control is preferable since it can alleviate the computational burden, avoid communication between different subsystems, and make the control more feasible and simpler. A power system is such a large-scale system where generators are interconnected through transmission lines. Decentralized control is therefore considered for power systems. In this paper, a robust decentralized excitation control scheme for interactions is proposed to enhance the transient stability of multimachine power systems. First we employ a DFL(Direct Feedback Linearization) compensator to rancel most of the nonlinearities; however, the resulting model still contains nonlinear interconnections. Therefore, we design a robust controller in order to deal with Interconnection terms. In this procedure, an upper bound of interconnection terms is estimated by an estimator. The resulting adaptive scheme guarantees the uniform ultimate boundedness of the closed-loop dynamic systems in the presence of the uncertainties.