• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.170-175
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• 2001

Q-learning is a kind of reinforcement learning where the agent solves the given task based on rewards received from the environment. Most research done in the field of Q-learning has focused on discrete domains, although the environment with which the agent must interact is generally continuous. Thus we need to devise some methods that enable Q-learning to be applicable to the continuous problem domain. In this paper, an extended fuzzy rule is proposed so that it can incorporate Q-learning. The interpolation technique, which is widely used in memory-based learning, is adopted to represent the appropriate Q value for current state and action pair in each extended fuzzy rule. The resulting structure based on the fuzzy inference system has the capability of solving the continuous state about the environment. The effectiveness of the proposed structure is shown through simulation on the cart-pole system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2819-2821
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• 2000

We introduce Adaptive Fuzzy Impedance Controller for force control when robot contact with environment. Because robot and environment was always effected by nonlinear conditions. it needs to deal with parameter's uncertainty. As. it induced Fuzzy system in impedance controller. it used fuzzy inference logic that has robustness about uncertainty to tune impedance controller stiffness gain. We applied adaptive fuzzy impedance controller in One-Link Robot system and the method shows a good performance on desired position and force control with intensional contacting environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.169-177
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• 1991

The Existing Protect schedulings are mathematical nodes upon which probability control is based. In fact, under the mechatronics environment in the new product design and development, statistical information is very poor or sometimes non-existent. Probabilistic PERT/CPM methods are not always satisfying because those methods suppose that it is possible to apply central- limit theorem and there exists a critical path which is much mart critical than all the other paths. Fuzzy project scheduling is possibility based scheduling. For this reason, the Fuzzy Project Scheduling essential to design, development and control the new product under the mechatranics environment. This paper deals with a modeling on the project scheduling which use fuzzy set theory. Fuzzy concepts in the project scheduling are shown to be very useful and easy to work with in the R & D system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.82-86
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• 2008

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.285-289
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• 1996

Fuzzy logic controllers have been shown better performance than conventional ones especially in highly nonlinear plants. These results are caused by the nonlinear fuzzy rules were not sufficient to cope with significant uncertainty of the plants and environment. Moreover, it is hard to make fuzzy rules consistent and complete. In this paper, we employed a predictive neural network to enhance the nonlinear inference capability. The predictive neural network generates predictive outputs of a controlled plant using the current and past outputs and current inputs. These predictive outputs are used in terms of fuzzy rules in fuzzy inferencing. From experiments, we found that the predictive term of fuzzy rules enhanced the inference capability of the controller. This predictive neural network can also help the controller cope with uncertainty of plants or environment by on-line learning.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.918-925
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• 2017

This paper presents a path planning problem for multi-robot system in the environment with dynamic obstacles. In order to guide the robots move along a collision-free path efficiently and reach the goal position quickly, a navigation method based on fuzzy logic controllers has been developed by using proximity sensors. There are two kinds of fuzzy controllers developed in this work, one is used for obstacle avoidance and the other is used for orientation to the target. Both static and dynamic obstacles are included in the environment and the dynamic obstacles are defined with no type of restriction of direction and velocity. Here, the environment is unknown for all the robots and the robots should detect the surrounding information only by the sensors installed on their bodies. The simulation results show that the proposed method has a positive effectiveness for the path planning problem.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.491-495
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• 1993

In this paper, we present a methodology to model an indoor environment of a mobile robot using fuzzy numbers and to make a global map of the robot environment using graph theory. We describe any geometric primitive of robot environment as a parameter vector in parameter space and represent the ill-known values of the prameterized geometric primitive by means of fuzzy numbers restricted to appropriate membership functions. Also we describe the spatial relations between geometric prinitives using graph theory for local maps. For making the global map of the mobile robot environment, the correspondence problem between local maps is solved using a fuzzy similarity measure and a Bipartite graph matching technique.

• Journal of Environmental Impact Assessment
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• v.15 no.6
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• pp.395-406
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• 2006

This study was carried out to offer the systematical and scientific method of regional environment conservation by deciding the rank using fuzzy theory, and try to find the methodology to accurately accomplished the regional environment assessment for sound land conservation. The results were as follows. To transform the Likert's scale granted to assessment indicators into the type of triangular fuzzy number (a, b, c), there was conversion to each minimum (a), median (b), and maximum (c) in applying membership function. We used the center of gravity and eigenvalue leading to the rank. In the sequential analysis of rank-based test of assessment indicators by triangular fuzzy number, the result proclaimed that ranking of the indicators was, in the biotic field, in the order of 'dominance', 'sociality', 'coverage' and in the abiotic one, 'soil pH', 'T-N', 'soil property', and in the qualitative one, 'impact rating class', 'hemeroby degree', 'land use pattern', and in the functional one, 'protection of water resource', 'offer of recreation', 'protection of soil erosion'. Therefore, there was a difference between subjective rank from human and the rank from triangular fuzzy number. In other words, the scientific rank decision would be not so much being subjective and biased as dealing with human thoughts mathematically by triangular fuzzy number.

• Coupled systems mechanics
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• v.2 no.3
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• pp.255-269
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• 2013

This paper targets to investigate the solution of fuzzy quadratic Riccati differential equations with various types of fuzzy environment using Homotopy Perturbation Method (HPM). Fuzzy convex normalized sets are used for the fuzzy parameter and variables. Obtained results are depicted in term of plots to show the efficiency of the proposed method.