A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

Li, Han-Xiong; Tong, Shaocheng

doi:10.1109/tfuzz.2002.806314

Cited by 274 publications

(4 citation statements)

References 24 publications

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“…In this section, we first explain the nonlinear direct adaptive control based on literatures [10,11,12,13,14,15], and then show how to develop a direct adaptive fuzzy controller for the two types of grid-connected inverters.…”

Section: Nonlinear Direct Adaptive Fuzzy Controlmentioning

confidence: 99%

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Single-phase three-level grid-connected inverter based on direct adaptive fuzzy control

Wang

Yang

2018

IEICE Electron. Express

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Single-phase three-level grid-connected inverter is widely used in high-voltage applications, which can be connected to the grid through a single inductor (L filter) or an LCL filter. The control schemes for an LCLbased grid-connected inverter are more complex than those for an L-based one to damp the resonance of the LCL filter. In this paper, a robust nonlinear direct adaptive fuzzy control scheme is proposed to control both the Land LCL-based inverters. The principle of the inverter is introduced, especially the space vector pulse width modulation method for the single-phase threelevel inverters. Then, the theory of the direct adaptive fuzzy control is described. Based on the theory, the current controller for both types of grid-connected inverters is designed. The closed-loop stability is proved based on the Lyapunov theory. Finally, simulations are conducted to verify the effectiveness of the controller. The results show that both the Land LCL-based grid-connected inverters operate well with strong robustness under the same controller.

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Section: Nonlinear Direct Adaptive Fuzzy Controlmentioning

confidence: 99%

“…Stable adaptive fuzzy control is proposed by Wang [10] and developed by Tong et al [11,12,13,14,15] to control the nonlinear systems, which does not require an accurate mathematical model of the system under control. The stable nonlinear adaptive fuzzy control consists of two types of schemes: direct control and indirect control.…”

Section: Introductionmentioning

confidence: 99%

Single-phase three-level grid-connected inverter based on direct adaptive fuzzy control

Wang

Yang

2018

IEICE Electron. Express

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“…and M x are the design parameters specified by the designer [10]. The global system stability is achieved by using the Lyapunov theory, that is, lim ( ) 0, The working process of the T2 adaptive fuzzy control is similar to that of the adaptive T1 fuzzy control except the inclusion of the type-reducer.…”

Section: Novel Type-adaptive Fuzzy Controllermentioning

confidence: 99%

Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

Zhou

Ying

Duan

2011

J. Cent. South Univ. Technol.

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A new adaptive Type-2 (T2) fuzzy controller was developed and its potential performance advantage over adaptive Type-1 (T1) fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noise or step disturbance indicate that the T2 controller is better than the T1 controller by 0−18%, depending on the experiment condition and performance measure.

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“…It is difficult to establish exactly mathematical model for the design of a model-based control system. In order to deal with this problem, the braches of current control theories are broad including classical control: neural networks (NNs) control [1][2][3], adaptive fuzzy logic control (FLCs) [4][5][6] or adaptive fuzzy-neural networks (FNNs) [7][8][9] etc. They are classified as adaptive intelligent control based on conventional adaptive control techniques where fuzzy systems or neural networks are utilized to approximate a nonlinear function of the dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

Self-Structured Organizing Single-Input CMAC Control for De-icing Robot Manipulator

Ngo¹,

Wang²,

Chen³

et al. 2011

ICA

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This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.

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A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems

Cited by 274 publications

References 24 publications

Single-phase three-level grid-connected inverter based on direct adaptive fuzzy control

Single-phase three-level grid-connected inverter based on direct adaptive fuzzy control

Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

Self-Structured Organizing Single-Input CMAC Control for De-icing Robot Manipulator

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