2015
DOI: 10.1155/2015/659521
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Adaptive Stabilization Control for a Class of Complex Nonlinear Systems Based on T-S Fuzzy Bilinear Model

Abstract: This paper proposes a stable adaptive fuzzy control scheme for a class of nonlinear systems with multiple inputs. The multiple inputs T-S fuzzy bilinear model is established to represent the unknown complex systems. A parallel distributed compensation (PDC) method is utilized to design the fuzzy controller without considering the error due to fuzzy modelling and the sufficient conditions of the closed-loop system stability with respect to decay rate are derived by linear matrix inequalities (LMIs). Then the er… Show more

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Cited by 2 publications
(2 citation statements)
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“…Logika fuzzy diterapkan dengan baik untuk mendefinisikan hubungan antara variabel input dan output sistem dengan menggunakan serangkaian logika atau aturan tertentu. Hal tersebut dapat diterapkan untuk proses industri yang kompleks menggunakan asumsi yang masuk akal dan prediksi kuantitatif [9] [10]. Sistem kendali fuzzy dikembangkan menggunakan seperangkat aturan yang telah ditetapkan dan menggabungkan sistem defuzzifikasi dan inferensi fuzzy untuk memprediksi perilaku suatu sistem [11].…”
Section: Logika Fuzzyunclassified
“…Logika fuzzy diterapkan dengan baik untuk mendefinisikan hubungan antara variabel input dan output sistem dengan menggunakan serangkaian logika atau aturan tertentu. Hal tersebut dapat diterapkan untuk proses industri yang kompleks menggunakan asumsi yang masuk akal dan prediksi kuantitatif [9] [10]. Sistem kendali fuzzy dikembangkan menggunakan seperangkat aturan yang telah ditetapkan dan menggabungkan sistem defuzzifikasi dan inferensi fuzzy untuk memprediksi perilaku suatu sistem [11].…”
Section: Logika Fuzzyunclassified
“…But we hope to improve analytic model predictive control algorithm to complete control of the system, because the method can avoid the calculation of online optimization predictive control, thus saving the amount of computation and reducing the complexity of solving the problem. Considering that the fuzzy system has the ability to approximate the unknown nonlinear system function and uncertainties, applying the fuzzy system to control nonlinear uncertain systems has 2 Mathematical Problems in Engineering become a hot spot theory and engineering research and made a lot of research results (see [12][13][14][15][16][17][18]).…”
Section: Introductionmentioning
confidence: 99%