Robust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems

Lee, Hyeongcheol

doi:10.1109/tfuzz.2010.2095859

Cited by 135 publications

(67 citation statements)

References 46 publications

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“…DC-DC converters adapting FLCs are used in two level and three level converters [27][28][29][30]. Fuzzy-tuned PI controllers are used in various DC-DC converters for the optimization of errors and reactive power control [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of PI, Fuzzy and Fuzzy tuned PI Controllers for Single-Phase AC-DC Three-Level Converter

Gnanavadivel¹,

Kumar²,

Yogalakshmi³

2017

Journal of Electrical Engineering and Technology

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-This paper presents the design of closed loop controllers operating a single-phase AC-DC three-level converter for improving power quality at AC mains. Closed loop inhibits outer voltage controller and inner current controller. Simulations of three level converter with three different voltage and current controller combinations such as PI-Hysteresis, Fuzzy-Hysteresis and Fuzzy tuned PIHysteresis are carried out in MATLAB/Simulink. Performance parameters such as input power factor and source current total harmonic distortion (THD) are considered for comparison of the three controller combinations. The fuzzy-tuned PI voltage controller with hysteresis current controller combination provides a better result, with a source-current THD of 0.93% and unity power factor without any source side filter for the three level converter. For load variations of 25% to 100%, a THD of less than 5% is obtained with a maximum value of only 1.67%. Finally, the fuzzy-tuned PI voltage with hysteresis controller combination is implemented in a Xilinx Spartan-6 XC6SLX25 FPGA board for experimental validation of power quality enhancement. A prototype 100 W, 0-24-48 V as output converter is considered for the testing of controller performance. A source-current THD of 1.351% is obtained in the experimental study with a power factor near unity. For load variations of 25% to 100%, the THD is found to be less than 5%, with a maximum value of only 2.698% in the experimental setup which matches with the simulation results.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of PI, Fuzzy and Fuzzy tuned PI Controllers for Single-Phase AC-DC Three-Level Converter

Gnanavadivel¹,

Kumar²,

Yogalakshmi³

2017

Journal of Electrical Engineering and Technology

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“…Robust adaptive control has been formulated for a class of uncertain nonlinear systems by output feedback control (Xu and Huang, 2010;Lee, 2011). For nonlinear systems in the strict-feedback form with unknown static parameters, a robust adaptive control law was designed by Montaseri and Mohammad (2012), which guarantees the asymptotic output tracking despite matched and unmatched uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy control for a class of constrained nonlinear systems with application to a surface vessel

Jamalzade

Koofigar

Ataei

2016

jtam

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In this paper, adaptive control for a class of uncertain nonlinear systems with input constraints is addressed. The main goal is to achieve a self-regulator PID controller whose coefficients are adjusted by using some adaptive fuzzy rules. The constraints on the control signal are taken into account as a saturation operator. The stability of the closed-loop system is analytically proved by using the Lyapunov stability theorem. The proposed method is then applied to a surface vessel with uncertain dynamic equations. The simulation results show the effectiveness of the proposed control strategy.

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“…Furthermore, approximation-based adaptive control has been investigated via the backstepping technique for nonlinear systems [2,7,25,42,44]. The adaptive neural control and adaptive fuzzy control are utilized to handle the unknown nonlinear systems [14,16,26,27,[34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Polynomial-Approximation-Based Control for Nonlinear Systems

Zhou

Chen

Lam

2016

Circuits Syst Signal Process

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This paper is concerned with the stabilization problem for nonlinear systems. A new polynomial-approximation-based approach for modeling nonlinear systems is first proposed. The nonlinearity is approximated by polynomials and the approximation errors are treated as modeling uncertainties. The original nonlinear systems are converted into polynomial systems with modeling uncertainties. In order to highlight the approximation accuracy, the piecewise polynomial approximation functions are utilized. A novel polynomial state-feedback controller is designed to solve the stabilization problem. Furthermore, switched polynomial state-feedback controllers are designed to improve the performance. The stabilization conditions are presented in terms of sum of squares, which can be numerically solved via SOSTOOLS. Finally, simulation examples are provided to demonstrate the feasibility of the proposed method and show its advantage over the polynomial-fuzzy-model-based approach.

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Robust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems

Cited by 135 publications

References 46 publications

Comparative Study of PI, Fuzzy and Fuzzy tuned PI Controllers for Single-Phase AC-DC Three-Level Converter

Comparative Study of PI, Fuzzy and Fuzzy tuned PI Controllers for Single-Phase AC-DC Three-Level Converter

Adaptive fuzzy control for a class of constrained nonlinear systems with application to a surface vessel

Polynomial-Approximation-Based Control for Nonlinear Systems

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