Design of a multi-layer fuzzy logic controller for multi-input multi-output systems

Tu, Kun-Hua; Lee, Tsu‐Tian; Wang, Wen-Jieh

doi:10.1016/s0165-0114(97)00410-7

Cited by 42 publications

(15 citation statements)

References 25 publications

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“…It manipulates vague concepts and provides a rational outcome. Fuzzy Logic has been extensively used in many commercial products where a precise mathematical model is not available [6], [7].…”

Section: Measurement Methods and Locationsmentioning

confidence: 99%

Muti-Layer Fuzzy Logic Sets for Mobile Path Loss in Forests

Phaiboon

Phokharatkul

Somkuarnpanit

2006

TENCON 2006 - 2006 IEEE Region 10 Conference

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Mobile path loss prediction in forests using Multi-Layer fuzzy logic system (MLFS) is presented in this paper. The MLFS consists of a tree density decision layer which is a supervisory layer in order to select the next layers using fuzzy decision. The sub-predictions use a set of rule base that provide path loss prediction in each case of an environment. These crisp inputs are classified by the fuzzifier to fuzzy sets and then inferenced using fuzzy linguistic rule base into multi -output path loss slopes via de-fuzzifier. For this study, we classified the terrains into high-, medium-, low-density and grass area and used the simple linguistic rules for prediction of the path loss slopes. We performed measurements in different forest densities at a frequency of 1.8 GHz with base station antenna height in a range of 3, 4, and 5 m above ground while the receiving antenna height was fixed at 1.8 m above ground. The results have shown that fuzzy logic approach provides more accurate prediction of path loss slopes than that of conventional empirical mathematic models. The proposed models will be useful for the local wireless network and micro-cell design of mobile communication systems in forests.

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Section: Measurement Methods and Locationsmentioning

confidence: 99%

Muti-Layer Fuzzy Logic Sets for Mobile Path Loss in Forests

Phaiboon

Phokharatkul

Somkuarnpanit

2006

TENCON 2006 - 2006 IEEE Region 10 Conference

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“…The use of the PID controller allowed to maintain the unloading force with the precision of approximately 30 N. The movement of this actuator is limited by limiting switches and its range of movement is approximately 10 cm. To give consideration to the tests that have been completed, a solution was proposed in which the PID controller's operation was coupled with a fuzzy logic controller [10,11,12]. The idea of study control algorithm is shown in Fig.…”

Section: Control Algorithm For Unloading Systemmentioning

confidence: 99%

Experimental Study on the Fuzzy-Pid Hybrid Control Algorithm for Unloading System in Mechatronic Device for Gait Re-Education

Duda¹,

Gembalczyk²

2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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In the article, the control system dedicated to the unit responsible for the patient unloading, which is one of the components of the mechatronic device for gait reeducation will be discussed and experimentally verified. The control algorithm of the unloading system should guarantee an expected constant value of the unloading force, regardless of the position and direction of the patient movement.The article presents a solutions in which the hybrid control algorithm based on fuzzy logic coupled with PID controller has been implemented. The optimization of parameters for regulators was conducted based on numerical simulations in which the walk of a patient through a step was analyzed. The experimental results show that the proposed control algorithm realized the primary goal (rapid reaction to the changes of the unloading force values) very well.

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“…Secondly, it was proven by Singh and Mulvaney (1994) and Wang and Tan (2000) that the food extrusion processes are essentially a multiple-input, multiple-output (MIMO) system and the MIMO control schemes are then required. A MIMO fuzzy system can be viewed as a set of multi-input single-output (MISO) fuzzy system (Tu, Lee, & Wang, 2000). A parallel connection fuzzy control structure using multiple MISO controllers is adopted in this design.…”

Section: Control Structurementioning

confidence: 99%