Fuzzy indirect adaptive control using SVM-based multiple models for a class of nonlinear systems

Zhou, Yonghua

doi:10.1007/s00521-012-1313-7

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…Considering Equation 33 and Equations 40 to 44, at time t = T, for the difference of the CEF E k over the iteration domain, we can get…”

Section: Part I: Difference Of E K (T)mentioning

confidence: 99%

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Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Jin

2017

Intl J Robust & Nonlinear

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Summary In this work, we propose a novel iterative learning control algorithm to deal with a class of nonlinear systems with system output constraint requirements and quantization effects on the system control input. Actuator faults have also been considered, which include multiplicative, additive, and stuck actuator faults. To the best of our knowledge, this is the first reported work in the iterative learning control literature to deal with quantization effects for the control input of nonlinear systems under the effects of actuator faults and system output constraints. Under the proposed scheme, using backstepping design and composite energy function approaches in the analysis, we show that uniform convergence of the state tracking errors can be guaranteed over the iteration domain, and the constraint requirement on the system output will not be violated at all time. In the end, a simulation study on a single‐link robot model is presented to demonstrate the effectiveness of the proposed scheme.

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“…Considering Equation 33 and Equations 40 to 44, at time t = T, for the difference of the CEF E k over the iteration domain, we can get…”

Section: Part I: Difference Of E K (T)mentioning

confidence: 99%

“…Remark The system model , which is sometimes referred to as the Brunovsky canonical form, is widely discussed in the nonlinear system control literature. For example, models for the flight of certain helicopters, chaotic systems, and electro‐hydraulic systems can be converted into the form of system model .…”

Section: Problem Formulationmentioning

confidence: 99%

Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Jin

2017

Intl J Robust & Nonlinear

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Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

Ghavidel

Kalat

2016

Neural Comput & Applic

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Adaptive tracking control for a class of stochastic nonlinear systems with full-state constraints and dead-zone

Peng

2023

Applied Mathematics and Computation

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Fuzzy indirect adaptive control using SVM-based multiple models for a class of nonlinear systems

Cited by 12 publications

References 14 publications

Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems

Adaptive tracking control for a class of stochastic nonlinear systems with full-state constraints and dead-zone

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