• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.572-580
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• 2013

IEEE 802.15.4e is a prospective standard for low latency control application in industries. This paper proposes a framework to evaluate the closed loop IEEE 802.15.4e based WNCS performance. The framework consists of two models: closed loop control system model and network model. The network model focuses on the PHY parameters of wireless link and takes the channel parameters into consideration. The PHY model combining with MAC model gives the control system model the probability of packet loss in a super-frame. In addition, redundancy mechanism is considered in IEEE 802.15.4e to reduce to data frame loss probability. The simulation is implemented in Matlab, PHY model takes the channel parameters from empirical results. Hence our evaluation gives insight into behavior of WNCS in different environments and it provides us a tool to design wireless network to achieve a good performance for control system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.705-711
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• 2019

Dynamic behaviors of the deployable composite reflector antenna are numerically and experimentally investigated. Equations of the motion are formalized using Kane's equation by considering multibody systems with two degrees of freedom such as folding and twisting angles. To interpret structural deformations of the reflector antenna, the composite reflector is modeled using a beam model with the FSDT(First-order Shear Deformation Theory). To determine design parameters such as a torsional spring stiffness and a damping coefficient depending on deployment duration, an inverted pendulum model is simply applied. Based on the determined parameters, dynamic characteristics of the deployable reflector are investigated. In addition, its results are verified and compared through deployment tests using a gravity compensation device.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.