• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.36-40
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• 1990

Conventional back propagation learning is generally characterized by slow and rather inaccurate learning which makes it difficult to use in control applications. A new multilayer perception architecture and its learning algorithm is proposed that consists of a Kohonen front layer followed by a back propagation network. The Kohonen layer selects a subset of the hidden layer neurons for local tuning. This architecture has been tested on the inverse kinematic solution of robot manipulator while demonstrating its fast and accurate learning capabilities.

• 한국정밀공학회지
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• 제22권4호
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• pp.92-98
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• 2005

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized teaming control based on adaptive control method. The original motivation of the teaming control field was loaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link.

• 제어로봇시스템학회논문지
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• 제12권10호
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• pp.1029-1034
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• 2006

In this paper, the tracking control of an automatic pipe cutting robot, called APCROM, with a magnetic binder is studied. Using magnetic force APCROM, a wheeled robot, binds itself to the pipe and executes unmanned cutting process. The gravity effect on the movement of APCROM varies as it rotates around the pipe laid in the gravitational field. In addition to the varying gravity effect other types of nonlinear disturbances including backlash in the driving system and the slip between the wheels of APCROM and the pipe also cause degradation in the cutting process. To maintain a constant velocity and consistent cutting performance, the authors adopt a repetitive learning controller (MRLC), which learns the required effort to cancel the tracking errors. An angular-position estimation method based on the MEMS-type accelerometer is also used in conjunction with MRLC to compensate the tracking error caused by slip at the wheels. Experimental results verify the effectiveness of the proposed control scheme.

• 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.

• 제어로봇시스템학회논문지
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• 제21권6호
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• pp.565-570
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• 2015

Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

• 전자공학회논문지S
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• 제34S권6호
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• pp.1-10
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• 1997

This paper presents an adaptive optimal learning controller for uncertian robot systems which makes use fo simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a lyapunov function, it is shown that all that error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is hsown by applying the controller to a 2-DOF robot manipulator.

• 제어로봇시스템학회논문지
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• 제3권4호
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• pp.363-372
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• 1997

In this paper, the design and the implementation of the adaptive learning rate neural network controller for an articulate robot, which is being developed (or) has been developed in our Automatic Control Laboratory, are mainly discussed. The controller reduces software computational load via distributed processing method using multiple CPU's, and simplifies hardware structures by the time-division control with TMS32OC31 DSP chip. Proposed neural network controller with adaptive learning rate structure using expert's heuristics can improve learning speed. The proposed controller verifies its superiority by comparing response characteristics of conventional controller with those of the proposed controller that are obtained from the experiments for the 5 axis vertical articulated robot. We, also, present the generalization property of proposed controller for unlearned trajectory and the change of load through experimental data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• 반도체디스플레이기술학회지
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• 제21권4호
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• pp.121-125
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• 2022

This paper deals with the development of a deep-learning-based robot that recognizes various types of stairs and performs a mission to go up to the target floor. The overall motion sequence of the robot is performed based on the ROS robot operating system, and it is possible to detect the shape of the stairs required to implement the motion sequence through rapid object recognition through YOLOv4 and Cuda acceleration calculations. Using the ROS operating system installed in Jetson Nano, a system was built to support communication between Arduino DUE and OpenCM 9.04 with heterogeneous hardware and to control the movement of the robot by aligning the received sensors and data. In addition, the web server for robot control was manufactured as ROS web server, and flow chart and basic ROS communication were designed to enable control through computer and smartphone through message passing.

• 제어로봇시스템학회논문지
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• 제3권4호
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• pp.350-356
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• 1997

It is desirable for autonomous robot systems to possess the ability to behave in a smooth and continuous fashion when interacting with an unknown environment. Although Q-learning requires a lot of memory and time to optimize a series of actions in a continuous state space, it may not be easy to apply the method to such a real environment. In this paper, for continuous state space applications, to solve problem and a triangular type Q-value model\ulcorner This sounds very ackward. What is it you want to solve about the Q-value model. Our learning method can estimate a current Q-value by its relationship with the neighboring states and has the ability to learn its actions similar to that of Q-learning. Thus, our method can enable robots to move smoothly in a real environment. To show the validity of our method, navigation comparison with Q-learning are given and visual tracking simulation results involving an 2-DOF SCARA robot are also presented.