• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2551-2554
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• 2003

In this paper, we describe the nonlinear character for a friction control. The nonlinear character of friction control is inherent in mechanical system, which has gained more and more interest. The modeling and compensation of nonlinear friction are difficult tasks for precise motion control. This paper is performance evaluation of nonlinearities and mechanical compliance exists together with friction control system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.38.1-38
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• 2001

In this paper, the vibration suppression problem of an axially moving power transmission belt is investigated. The equations of motion of the moving belt is first derived by using Hamilton´s principle for systems with changing mass. The total mechanical energy of the belt system is considered as a Lyapunov function candidate. Using the Lyapunov second method, a nonlinear boundary control law that guarantees the uniform asymptotic stability is derived. The control performance with the proposed control law is simulated. It is shown that a boundary control can still achieve the uniform stabilization for belt systems.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.374-378
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• 2003

A new discrete-time fuzzy output feedback control method for nonlinear systems with unknown time-delay is proposed. Ma et al. proposed an analysis and design method of fuzzy controller and observer and Cao et al. extend this result to be applicable fir the nonlinear systems with known time-delay. For the case of unknown time-delay, we derive the sufficient condition f3r the asymptotic stability of the equilibrium Point by applying Lyapunov-Krasovskii theorem and convert this condition into the LMI problem.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.575-580
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• 2003

In this paper, we develop a control hardware such as an FPGA based general purpose controller with a DSP board to solve nonlinear control problems. PID control algorithms are implemented in an FPGA and neural network control algorithms are implemented in a DSP board. PID controllers implemented on an FPGA was designed by using VHDL to achieve high performance and flexibility. By using high capacity of an FPGA, the additional hardware such as an encoder counter and a PWM generator, can be implemented in a single FPGA device. As a result, the noise and power dissipation problems can be minimized and the cost effectiveness can be achieved. In order to show the performance of the developed controller, it was tested for controlling nonlinear systems such as an inverted pendulum.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.247-253
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• 2000

In this paper, we deal with the full range control problem of nonlinear boiler systems subject to complex actuator constraints. Firstly, $H\infty$ loop shaping design procedure［10］ is used for the controller design. Secondly, modified high-gain feedback［11］ for the loop shaping controller is adopted for the anti-windup function and the bumpless transfer technique between controllers is proposed for the full range control of nonlinear systems. Finally, the performance of the proposed controller is demonstrated through the simulation studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.444-444
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• 2000

For a class of nonlinear systems which satisfy a certain condition so called output feedback exponential passivity (OFEP), it is well known that one can easily design a high-gain output feedback control system. The designed high-gain controller has simple structure and high robustness. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper. we deal with a design problem of the robust high-gain adaptive output feedback control for the above-mentioned class of nonlinear systems with uncertain nonlinearities and/or disturbances.

• 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 Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.177-182
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• 2003

The robust output tracking control problem of general nonlinear MIMO systems is discussed. The robustness against parameter uncertainties is considered. In this paper, we proposed the robust output tracking control scheme for a class of MIMO nonlinear dynamical systems using output feedback linearization method. The presented control scheme is based on the VSS. We assume that the nonlinear dynamical system is minimum phase, the relative degree of the system is r$_{1}$＋r$_{2}$＋…r$_{m}$$\leq$ n and zero dynamics is stable. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. And we verified that the proposed control scheme is then applied to the control of a two degree of freedom (DOF) robotic manipulator with payload.d.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.621-623
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• 2000

In this paper, the effective design method of the predictive controller using fuzzy neural networks(FNNs) is presented for the Intelligent control of chaotic nonlinear systems. In our design method of controller, predictor parameters are tuned by the error value between the actual output of a chaotic nonlinear system and that of a fuzzy neural network model. And the parameters of predictive controller using fuzzy neural network are tuned by the gradient descent method which uses control error value between the actual output of a chaotic nonlinear system and the reference signal. In order to evaluate the performance of our controller, it is applied to the Duffing system which are the representative continuous-time chaotic nonlinear systems and the Henon system which are representative discrete-time chaotic nonlinear systems.