A Sliding Mode Control Using Fuzzy-Neural Hierarchical Multi-model Identifier

Baruch, Ieroham S.; Guzman, Jose-Luis Olivares; Mariaca-Gaspar, Carlos-Roman; Galván-Guerra, Rosalba

doi:10.1007/978-3-540-72434-6_77

Cited by 4 publications

(8 citation statements)

References 17 publications

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“…In [12], [13], [14], Baruch et al defined an indirect Fuzzy Neural Multi-Model (FNMM) control system, based on Takagi-Sugeno (T-S) fuzzy rules, [15], containing in its consequent parts computational procedure of a Backpropagation Learning (BP) of a Recurrent Trainable NN (RTNN), [14]. Here the indirect adaptive neural control is realized as a Sliding Mode Control (SMC) using a fuzzyneural identifier.…”

Section: Decentralized Indirect Adaptive Fuzzy-neural Multi-model Conmentioning

confidence: 99%

“…The reduced plant model (1)-(10) (here (3) represented the OD equations of the recirculation tank), could be used as unknown plant model which generate input/output process data for decentralized adaptive FNMM control system design, based on the concept, given in [11], [14], [16]. The mentioned concept could be applied for this DPS, fuzzyfying the space variable z, which represented the height of the fixed bed.…”

Section: Analytical Model Of the Anaerobic Digestion Bioprocessmentioning

confidence: 99%

“…The structure of the entire control system, [14], tracking. The upper hierarchical level of the FNMM control system is one layer perceptron which represented the control defuzzyfier, [14]. The hierarchical FNMM controller has two levels -Lower Level of Control (LLC), and Upper Level of Control (ULC).…”

Section: An Indirect Adaptive Fnmm Control System Designmentioning

confidence: 99%

“…3. It contained a space plant output fuzzyfier and four identification T-S fuzzy rules, labeled as RI i , which consequent parts are learning procedures, [14]. The identification T-S fuzzy rules have the form:…”

Section: An Indirect Adaptive Fnmm Control System Designmentioning

confidence: 99%

“…4. It contained a spaced plant reference fuzzyfier and four sliding mode control T-S fuzzy rules, which consequent parts are SMC procedures, [14], using the state, and parameter information, issued by the corresponding identification rules. The control T-S fuzzy rules have the form:…”

Section: An Indirect Adaptive Fnmm Control System Designmentioning

confidence: 99%

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Decentralized indirect adaptive Fuzzy-Neural Multi-Model control of a distributed parameter bioprocess plant

Baruch

Galván-Guerra

Mariaca-Gaspar

et al. 2008

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

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The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are implemented by a Hierarchical Fuzzy-Neural Multi-Model Sliding Mode Controller (HFNMM-SMC). The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking outperforming the optimal control.

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