• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1679-1687
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• 2016

Mobile robots are effectively used in industrial fields that require flexible manufacturing systems. Mobile robots have to move with mechanical loads such as product parts along the specified paths, and are usually equipped with kinematic controllers. When the loads and nonlinear frictions are too high, satisfactory control performances can not be expected with the kinematic controllers, so some dynamic controllers have been developed. Conventional dynamic controllers require the exact weights and locations of the loads; however, the loads are frequently changed and unknown so that the control performances of the conventional controllers are limited. This paper proposes an adaptive PD control method using gradient descent learning to have sufficient dynamic control performance for unknown loads. Simulation studies have been conducted for various load conditions to verify that the adaptive PD control method have much broader convergence region than the convention method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.394-396
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• 1992

This note is concerned with the point to point control of manipulators having elastic joints. We present a PD control algorithm which is adaptive with respect to the gravity and elastic parameters of robot manipulators. While the conventional control law is used, a new adaptive law is used to improve the performance. The proposed controller is shown to be stable. It is Shown that steady-state position error converges to zero through some simulations concerning the manipulator with three revolute elastic joints.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.2
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• pp.38-44
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• 1994

Control Algorithms of the Auto-Pilot system have been studied for the navigational economics and crew's comfortability since 1960's, when Auto-Pilot system was installed on the trans-ocean ships. At the beginning the PD control algorithm was used with the weather adjust function introduced to reduce the response of the auto-pilot system to the high frequency wave excitation in rough sea. In this study, the optimal and adaptive control theories are applied for the auto-pilot control algorithm. And those two algorithms are compared through the pre-defined cost function to obtain the most effective control technique for the Auto-Pilot system. The parameterization of the ship meneuvering equation for the adaptive control algorithm design procedure was examined and the advantage of the adaptive control was found through the simulation result with the wrong initial parameter value.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.490-492
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• 1998

In this paper, fuzzy-PD control system is designed to control angle and position of the inverted pendulum. To optimize parameters of fuzzy-PD controller, we used adaptive evolutionary computation(AEC). AEC uses a Genetic A1gorithm(GA) and an Evolution Strategy(ES) in an adaptive manner in order to take merits of two different evolutionary computations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.4
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• pp.335-341
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• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.291-294
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• 2004

Many researches have been focused on optimal designs of a pole shape in order to reduce cogging torques, which are generated between permanent magnets and slots. In this paper, an adaptive controller is proposed for reducing the effect of cogging torques in servo motors. The controller stabilizes the control system and shows an excellent trajectory tracking performance compared to the conventional PD controller.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.70-75
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• 2005

Many researches have been focused on optimal designs of a pole shape in order to reduce cogging torques, which are generated between permanent magnets and slots. In this paper, an adaptive controller is proposed fur reducing the effect of cogging torques in servo motors. The controller stabilizes the control system and shows an excellent trajectory tracking performance compared to the conventional PD controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.102-108
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• 2008

This paper presents robust and adaptive control method for complicated three dimensional crane systems with uncertain effect. We consider an overhead crane system in which a trolly located on its top is moved to x- and y-axis independently. We first approximate the complicated crane model through linearization approach to simply construct a PD control and then design an adaptive control system for compensating modeling error and control deviation which is feasibly occurred due to system perturbation in practice. An adaptive control scheme is analytically derived using Lyapunov stability theory for a given bound of system perturbation. We accomplish numerical simulation for evaluation of the proposed control system and demonstrate its superiority comparing with the traditional control strategy.