• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1597-1600
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• 2010

In this paper, a direct controller for nonlinear plants using a neural network is presented. The composed of the control input by using RBF neural networks and auxiliary input to compensate for effects of the approximation errors and disturbances. In the results, using this scheme, the output tracking error between the plant and the reference model can asymptotically converge to zero in the presence of bounded disturbances and approximation errors. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• Proceedings of the KIEE Conference
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• 1997.07a
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• pp.115-117
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• 1997

본 논문에서는 유도전동기의 적응 비선형 제어에 대해 논한다. 제어 목적은 회전자저항의 불확실성에도 불구하고 유도전동기의 속도 및 자속을 분리하여 제어하는 것이며, 이를 위해 백스테핑(Backstepping) 기법을 사용한 비선형제어기와 회전자 저항 추정기를 결합한다. 제안된 적응제어시스템은 내부의 모든 변수가 유계(Bounded)되어 있다는 점에서 안정하며, 더불어 그 개선된 성능을 모의실험을 통해 보인다.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.223-233
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• 2008

A new output feedback controller design approach for flexible-joint (FJ) robots via the observer dynamic surface design technique is presented. The proposed approach only requires the feedback of position states. We first design an observer to estimate the link and actuator velocity information. Then, the link position tracking controller using the observer dynamic surface design procedure is developed. Therefore, the proposed controller can be simpler than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop system are uniformly ultimately bounded. Finally, the simulation results of a three-link FJ robot are presented to validate the good position tracking performance of the proposed control system.

• International Journal of Control, Automation, and Systems
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• v.6 no.2
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• pp.171-179
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• 2008

This paper addresses the synthesis of an iterative learning controller for a class of linear systems with norm-bounded parameter uncertainties. We take into account an iterative learning algorithm with current cycle feedback in order to achieve both robust convergence and robust stability. The synthesis problem of the developed iterative learning control (ILC) system is reformulated as the ${\gamma}$-suboptimal $H_{\infty}$ control problem via the linear fractional transformation (LFT). A sufficient convergence condition of the ILC system is presented in terms of linear matrix inequalities (LMIs). Furthermore, the ILC system with fast convergence rate is constructed using a convex optimization technique with LMI constraints. The simulation results demonstrate the effectiveness of the proposed method.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.108-114
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• 2002

This paper describes the design of a robust adaptive controller for a nonlinear dynamical system with unknown nonlinearities. These unknown nonlinearities are approximated by multilayered neural networks (MNNs) whose parameters are adjusted on-line, according to some adaptive laws far controlling the output of the nonlinear system, to track a given trajectory. The main contribution of this paper is a method for considering input constraint with a rigorous stability proof. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive MNN model. An overall control system guarantees that the tracking error converges at about zero and that all signals involved are uniformly bounded even in the presence of input saturation. Theoretical results are illustrated through a simulation example.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.31-37
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• 2003

A robust tracking control for nonholonomic dynamic systems is proposed in this paper. Since nonholonomic dynamic systems have constraints imposed on motions that are not integrable, i.e., the constraints cannot be written as time derivatives of some functions of generalized coordinates, advanced techniques are needed for their control. It is shown that if the state of nonholonomic systems is mapped into a bounded space by a coordinate transformation, a robust controller for dynamic models of nonholonomic systems with input disturbances can be designed using sliding mode control. Stability and robustness of the proposed controller are proved in the Lyapunov sense. Numerical simulations on the trajectory tracking of a two-wheeled mobile robot are conducted to validate the effectiveness of the proposed controller.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.121-127
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• 2009

This paper deals with the design problem of robust stabilization and non-fragile controller for discrete-time singular systems with parameter uncertainties and time-varying delays in state and input by delay-dependent Linear Matrix Inequality (LMI) approach. A new delay-dependent bounded real lemma for singular systems with time-varying delays is derived. Robust stabilization and robust non-fragile state feedback control laws are proposed, which guarantees that the resultant closed-loop system is regular, causal and stable in spite of time-varying delays, parameter uncertainties, and controller gain variations. A numerical example is given to show the validity of the design method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.10
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• pp.859-867
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• 2015

This study proposes a robust formation flight control technique of multiple unmanned aerial vehicles(UAVs) using behavior-based decentralized approach. The behavior-based decentralized method has various advantages because it utilizes information of neighboring UAVs only instead of information of whole UAVs in the formation maneuvering. The controllers in this paper are divided into two methods: first one is based on position and velocity of neighboring UAVs, and the other one is based on position of neighboring UAVs and passivity technique. The proposed controllers assure uniformly ultimate boundedness of closed-loops system under time varying bounded disturbances. Numerical simulations are performed to validate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.581-586
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• 2003

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neurocontroller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.1000-1006
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• 2007

This paper presents an LMI-based method to design a saturated state-feedback $H_2$ controller for uncertain systems with actuator saturation. Specifically, the paper proposes a sufficient condition such that the system under norm-bounded uncertainties and actuator saturation is asymptotically stable and the $H_2$-norm of the system has an upper-bound. The resulting condition is further utilized to solve a convex optimization problem specified in the context of $H_2$-norm minimization, whose solution yields a saturated $H_2$ controller. A numerical example is presented to show the effectiveness of the proposed method.