• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.218-223
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• 2012

Maintenance and improvement of the graph connectivity is very important for decentralized multi-agent systems. Although the CBBA (Consensus-Based Bundle Algorithm) guarantees suboptimal performance and bounded convergence time, it is only valid for connected graphs. In this study, we apply a decentralized estimation procedure that allows each agent to track the algebraic connectivity of a time-varying graph. Based on this estimation, we design a decentralized gradient controller to maintain the graph connectivity while agents are traveling to perform assigned tasks. Simulation result for fully-actuated first-order agents that move in a 2-D plane are presented.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.13 no.3
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• pp.252-259
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• 2008

The deep-sea mining system is generally composed of surface vessel, lifting system, buffer, flexible pipe and miner. The mining system can be regarded as a large-scale system in which each subsystem is interconnected to other ones. In order to control a large-scale system, decentralized control approaches have been proposed recently. In this paper, as a basic study on application of decentralized control, firstly, the mining system was modeled in a simplified way. Lifting system and buffer were regarded as a spherical pendulum and the flexible pipe was taken as a two-dimensional linear spring connection. Based on the simplified model dynamics, the mining system can be decentralized two subsystems, the one consisting of surface vessel, lifting system and buffer, and the other, the miner. Next, this paper proposed the design of controller for each decentralized subsystem by regarding the interacting terms as disturbances. The controllers kept the constant distance between two subsystems during the miner was moving on the specified track. Finally, the efficiency of proposed controller was proven through the numerical simulation of the derived model.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.208-216
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• 1996

This paper presents a decentralized control scheme for interconnected systems with unmodeled nonlinearities and interactions using a neural coordinator. The interactions due to the interconnection and the unmodeled nonlinearity associated with each subsystem are represented by the deviations from linearized states of decomposed subsystems. the decentralized controller is composed of local controllers and a neural coordinator. The local controller for each subsystem is derived from linearized local system parameters y linear optimal control theory. the neural cooridnator generates a corrective control signal to cancel the effect of deviation sthrough the backpropagation learning with the rrors obtained form the difference of the local system outputs and reference model outputs. the reference model consists of the part of local system without deviations. The effectiveness of the proposed control scheme is demonstrated by simulation studies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1450-1454
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• 2005

In this paper, the control strategy of the tension and speed based a prototype bio-wrap winding machine is developed. The decentralized control strategy using PID control algorithm applied for each subsystem is proposed to control the each system's desired outputs, because the tension of each subsystem effects that of next roll system. The computer simulations and the experiment results are presented to show that the proposed control scheme is feasible for a prototype bio-wrap winding machine.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.

• Journal of Korea Society of Industrial Information Systems
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• v.1 no.1
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• pp.153-176
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• 1996

The new field of learning control devleops 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 concepts as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers ina 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.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.207-214
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• 2010

This paper proposes about decentralized control approach based Leader-Follower formation control using LOS (Line of Sight) algorithm and unknown input observer. The position of robots which is a basic information in multi-robot or single robot motion control is determined by localization algorithm fusing UPS (Ultrasonic Position System) and kinematics model. For formation control, a decentralized control approach individually installing a local controller in leader and follower robot is adopted. Leader robot is controlled to track a specified trajectory by LOS algorithm, and the other robots follow the leader by local controller based on tracking platoon level function, self-sensing data and estimated information from unknown input observer. The performance of proposed method is proven through the formation experiment of two vehicle models.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.418-429
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• 2018

This paper studies the decentralized position/force control problem for constrained reconfigurable manipulator without torque sensing. A novel joint torque estimation scheme that exploits the existing structural elasticity of the manipulator joint with harmonic drive model is applied for each joint module. Based on the estimated joint torque and dynamic output feedback technique, a decentralized position/force control strategy is presented. In order to solve the problem of controller parameter perturbation, the non-fragile robust technique is introduced into the dynamic output feedback controller. Subsequently, the stability of the closed-loop system is proved using the Lyapunov theory and linear matrix inequality (LMI) technique. Finally, two 2-DOF constrained reconfigurable manipulators with different configurations are applied to verify the effectiveness of the proposed control scheme in numerical simulation.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.5
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• pp.64-72
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• 2004

An extension of interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupted by both process and output disturbances is presented. The teaming control develops controllers that learn to improve their performance at executing a given task, based on experience performing this task. The simplest forms of loaming control are based on the same concept as integral control, but operating in the domain of the repetitions of the task. This paper studies the use of such controllers in a decentralized system, such as a robot moving on the vertical plane with the controller for each link acting independently. The basic result of the paper is to show that stability and iterative precision of the learning controllers for all subsystems when the coupling between subsystems is turned off, assures stability of the decentralized teaming in the coupled system, provided that the sample time in the digital teaming controller is sufficiently short. The methods of teaming system are shown up for the iterative precision of each link.