• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 추계학술대회 논문집
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• pp.301-305
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• 2001

Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking(or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, complex, coupled manipulator dynamics. The minimum-time manipulator control problem is solved for the case when the path is specified and the actuator torque limitations are known. In path planning, DP is applied to applied to find the shortest path form initial position to final position with the assumptions that there is no obstacle and that each path is straight line. In path control, the phase plane technique is applied to the minimum-time control with the assumptions that the bound on each actuator torque is a function of joint position and velocity or constant. This algorithm can be used for any manipulator that has rigid link, known dynamics equations of motion, and joint angles that can be determined at a given position on the path.

• 동력기계공학회지
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• 제2권3호
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• pp.74-82
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• 1998

This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.285-287
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• 2006

The paper treats the nonlinear robust control of Chua's circuit having Chuar's diode as an element based on the internal model principle. The Chua's diode has unknown nonlinear parameters and the circuits parameters are alos assumend unknown. Nonlinear regulator equations are established to obtain 3-fold equilibrium equations on which the output error is zero. Also an internal model of the 3-fold exosystem is constructed for obtaining the control law. Pole Placement method is used for obtaining the feeback control law. Simulation results are presented for tracking the sinusoidal and constant reference input signal. Asymptotic trajectory control and the suppression of chaotic motion in spite of uncertainties in the system are accomplished.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.133-139
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• 1997

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is propored a learning controller consisting of two neural network-fuzzy based on independent resoning and a connection net with fixed weights to simply the neural network-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.162.1-162
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• 2001

In this paper, the adaptive fuzzy system is used as an adaptive approximator for robot nonlinear dynamic. A theoretical justification for the adaptive approximator is proving that if the representive point(RP or switching function) and its derivative in sliding mode control are used as the inputs of the adaptive fuzzy system, the adaptive fuzzy system can approximate robot nonlinear dynamics in the neighborhood of the switching surface. Thus the fuzzy controller design is greatly simplified and at the same time, the fuzzy control rule can be obtained easily by the reaching condition. Based on this, a new method for designing an adaptive fuzzy control system based on sliding mode is proposed for the trajectory tracking control of a robot with unknown nonlinear dynamics.

• 한국해양공학회지
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• 제11권1호
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• pp.84-95
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• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• KIEE International Transaction on Systems and Control
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• 제2D권2호
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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.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 춘계학술대회 논문집
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• pp.210-215
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• 1999

In this paper, a new approach to the dynamic control technique for track vehicle system using fuzzy-neural network control technique is proposed. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.69.3-69
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• 2001

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 한국정밀공학회지
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• 제13권4호
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• pp.75-86
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• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.