• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.465-470
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• 2012

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.694-697
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• 1996

The aim of this work for 5 years from 1994 is to develop a multi-fingered robot hand and its control system for grasp and manipulation of objects dexterously. Since the robot hand is still being developed, a commercialized robot hand from Barrett Company is utilized to implement a hand controller and control algorithm. For this, VME based motion control and interface boards are developed and multi-sensors such as encoder, force/torque sensor, dynamic sensor and artificial skin sensor are partly developed and employed for the grasping control algorithm. In oder to handle uncertainties such as mechanical idleness and backlash, a fuzzy rule based grasping algorithm is also considered and tested with the developed control system.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1337-1338
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• 2015

In this paper, a new control technique using WIPDC(Weighted Integral Parallel Distributed Compensation) and ISMC(Integral Sliding Mode Control) is proposed for high performance and robust trajectory tracking control of a wheeled mobile robot. The WIPDC reduces the steady-state error by adding a weighted integral controller to the PDC. So, the trajectory tracking control using the WIPDC can obtain more accurate control performance than the PDC. And the ISMC based control input gives the mobile robot to preserve the system dynamics controlled by the WIPDC control input in spite of external disturbances. Therefore, the proposed control method shows a robust and precise trajectory tracking performance.

• Journal of Mechanical Science and Technology
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• v.15 no.3
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• pp.320-326
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• 2001

A Robust controller is designed for cascaded nonlinear uncertain systems that can be decomposed into two subsystems; that is, a series connection of two nonlinear subsystems, such as a robot manipulator with actuators. For such systems, a recursive design is used to include the second subsystem in the robust control. The recursive design procedure contains two steps. First, a fictitious robust controller for the first subsystem is designed as if the subsystem had an independent control. As the fictitious control, a nonlinear H(sub)$\infty$ control using energy dissipation is designed in the sense of L$_2$-gain attenuation from the disturbance caused by system uncertainties to performance vector. Second, the actual robust control is designed recursively by Lyapunovs second method. The designed robust control is applied to a robotic system with actuators, is which the physical control inputs are not the joint torques, but electrical signals to the actuators.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.717-719
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• 2004

Currently mobile devices change rapidly our life and they have considerable influences over many parts of our society. If the mobile device is applied to a control system, the usability of the control system is increased with its convenient accessibility and mobility. This paper realizes a motor control system by using a mobile device. The device uses Intel Xscale PXA-250 in which Widows CE is ported. The device is very popular at the applications of mobile devices. Also we consider its application to a mobile robot such as home service robot.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.43-53
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• 2002

Polishing a die that has free-form surfaces is a time-consuming and tedious job, and requires a considerable amount of high-precision skill. In order to reduce the polishing time and cope with the shortage of skilled workers, a user-friendly automatic polishing system was developed. The polishing system is composed of two subsystems, a three-axis machining center and a two-axis polishing robot. The system has five degrees of freedom and is able to keep the polishing tool in a position normal to the die surface during operation. A sliding mode control algorithm with velocity compensation was proposed to reduce tracking errors. Trajectory tracking experiments showed that the tracking error can be reduced prominently by the proposed sliding mode control compared to a PD (proportional derivative) control. To evaluate the polishing performance of the polishing system and to and the optimal polishing conditions, the polishing experiments were conducted.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.869-879
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• 1995

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of learning control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. In the previous paper, I had studied the use of such controllers in a decentralized system, such as a robot with the controller for each link acting independently. The basic result of the paper is to show that stability of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized learning in the coupled system, provided that the sample time in the digital learning controller is sufficiently short. In this paper, we present two examples. The first illustrates the effect of coupling between subsystems in the system dynamics, and the second studies the application of decentralized learning control to robot problems. The latter example illustrates the application of decentralized learning control to nonlinear systems, and also studies the effect of the coupling between subsystems introduced in the input matrix by the discretization of the system equations. The conclusion is that for sufficiently small learning gain, and sufficiently small sample time, the simple learning control law based on integral control applied to each robot axis will produce zero tracking error in spite o the dynamic coupling in the robot equations. Of course, the results of this paper have much more general application than just to the robotics tracking problem. Convergence in decentralized systems is seen to depend only on the input and output matrices, provided the sample time is suffiently small.