• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.223-225
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• 2006

A ball moving on a beam is a typical nonlnear dynamic system, which is often adopted to proof test diverse control schemes. Ball and plate system is the extension of the traditional ball and beam problem that moves a metal ball on a rigid plate. In this paper, a trajectory planning and tracking problem is proposed for ball and plate system, which is to control the ball from a point to another without hitting the obstacles. Our scheme is composed of three controllers, TS type optimal path tracking controller, mandani type obstacle avoidance controller and trajectory planning controller that determines the desired trajectory. But this type of construction can give rise to chattering executions. Because the difference of contributions from concurrent controllers can cause behaviors unsmoothly. We propose fuzzy pid supervision control1er to handle this problem.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.21-26
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• 2001

In this study, we propose a two degree of freedom robust output tracking control method for a class of nonlinear system. We consider hyperbolically nonminimum phase single-input single-output uncertain nonlinear systems. We also consider the case that the nominal input-state equation is differentially flat. Nominal stable state trajectory is obtained in the flat output space via the flat output. Nominal feedforward control input is also computed from the nominal state trajectory. Due to the nature of the method, the generated flat output trajectory and control input are noncausal. Robust feedback control is designed to stabilize the systems around the nominal trajectory. A numerical example is given is given to demonstrate that robust tracking is achieved.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.561-567
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• 2006

This paper deals with a fuzzy controller using IA(Immune Algorithm) for Trajectory Tracking of 2-DOF WMR(Wheeled Mobile Robot). The global inputs to the WMR are reference position and reference velocity, which are time variables. The global output of WMR is a current position. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities on the track of trajectory is necessary. Therefore, a FIAC(Fuzzy-IA controller) is proposed to give velocity compensation in this system. Input variables of fuzzy part are position errors in every sampling time. The output values of fuzzy part are compensation velocities. IA are implemented to adjust the scaling factor of fuzzy part. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.10
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• pp.110-118
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• 2000

An adaptive controller for trajectory tracking control of a pneumatic cylinder is proposed. The controller is directly derived by using Lyapunov function, and very simple and computationally efficient since it does not require the mathematical model or the parameter values of a pneumatic system. It is also shown that the system is bounded stable with the controller, and the size of tracking errors can be made arbitrarily small. The stability and the performance of the controller is also verified experimentally. The results of the experiments demonstrate that the proposed controller achieves more accurate trajectory tracking performance than a PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.912-915
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• 1996

The computed-torque method (CTM) shows good trajectory tracking performance in controlling robot manipulator if there is no disturbance or modelling errors. But with the increase of a payload or the disturbance of a manipulator, the tracking errors become large. So there have been many researches to reduce the tracking error. In this paper, we propose a new control algorithm based on the CTM that decreases a tracking error by generating new reference trajectory to the controller. In this algorithm we used the concept of sliding mode theory and fuzzy system to reduce chattering in control input. For the numerical simulation, we used a 2-link robot manipulator. To simulate the disturbance due to a modelling uncertainty, we added errors to each elements of the inertia matrix and the nonlinear terms and assumed a payload to the end-effector. In this simulation, proposed method showed better trajectory tracking performance compared with the CTM.

• Journal of Mechanical Science and Technology
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• v.19 no.5
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• pp.1107-1115
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• 2005

In this study, a position and force simultaneous trajectory tracking control algorithm is proposed for a driving apparatus that consists of two pneumatic cylinders connected in series. The controller applied to the driving apparatus is composed of a non-interaction controller to compensate for interaction between cylinders and a disturbance observer aimed to reduce the effect of model discrepancy that cannot be compensated by the non-interaction controller. The effectiveness of the proposed control algorithm is proved by experimental results.

• Journal of Power System Engineering
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• v.10 no.3
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• pp.97-103
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• 2006

In this study, a position trajectory tracking control algorithm is proposed for a pneumatic artificial muscle driving apparatus composed of a actuator which imitates the muscle of human, a position sensor and a control valve. The controller applied to the driving apparatus is composed of a state feedback controller and disturbance observer. The feedback controller which feeds back position, velocity and acceleration is derived from the linear model of pneumatic artificial muscle driving apparatus. The disturbance observer is designed to improve trajectory tracking performance and to reduce the effect of model discrepancy. The effectiveness of the designed controller is proved by experiments and the experimental results show that the pneumatic artificial muscle driving apparatus with the proposed control algorithm tracks given position reference inputs accurately.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1120-1125
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• 2007

Experimental study on the control of randomly disturbing system is conducted. External and internal disturbances are imposed to the system in combined manner. A vertical propeller system exposed horizontal weak turbulent air flow is chosen as an experimental model. The aim of the control system is to maintain the angular position of vertical propeller in parallel to air flow. Trajectory Tracking Stochastic Controller (TTSC) is designed to ensure system's stability while following system command. The Trajectory Tracking Stochastic Controller is composed of two controller, one is stochastic controller to suppress internal random noise and the other one is trajectory-tracking controller to follow the command having random noise. The proposed hybrid controller, TTSC, shows remarkable performance in pitch control of vertical propeller system in wind-tunnel test

• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.69-77
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• 2009

In this study an ANFIS-based trajectory tracking motion control algorithm is proposed for autonomous garage and parallel parking of a model car. The ANFIS controller is trained off-line using data set which obtained by Mandani fuzzy inference system and thereby the processing time decreases almost in half. The controller with a steering delay compensator is tuned through simulations performed under MATLAB/Simulink environment. Experiments are carried out with the model car for garage and parallel parking. The experimental results show that the trajectory tracking performance is satisfactory under various initial and road conditions

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.980-984
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• 1990

In order to find the optimal control law for the precise trajectory tracking of a robot manipulator, a perturbational control method is proposed based on a linearized manipulator dynamic model which can be obtained in a very compact and computationally efficient manner using the dual number algebra. Manipulator control can be decomposed into two parts: the nominal control and the corrective perturbational control. The nominal control is precomputed from the inverse dynamic model using the quantities of a desired trajectory. The perturbational control is obtained by applying the second-variational method on the linearized dynamic model. Simulation results for a PUMA-560 robot show that, by using this controller, the desired trajectory tracking performance of the robot can be achieved, even in the presence of large initial positional disturbances.