Preventing bursting in approximate-adaptive control when using local basis functions

DOI10.1016/J.FSS.2008.03.021zbMath1175.93131OpenAlexW2003217710MaRDI QIDQ1037871

C. J. B. Macnab

Publication date: 17 November 2009

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2008.03.021

zbMATH Keywords

fuzzy controlneuro-fuzzy systemsroboticscerebellar model articulation controllerneural-adaptive controladaptive-fuzzy controlapproximate-adaptive control

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Adaptive control/observation systems (93C40) Automated systems (robots, etc.) in control theory (93C85)

Related Items (2)

Creating a CMAC with overlapping basis functions in order to prevent weight drift ⋮ Control of robots using radial basis function neural networks with dead-zone

Cites Work

• Feedback error learning and nonlinear adaptive control
• Instability analysis and improvement of robustness of adaptive control
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• Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form
• Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints
• An Eclectic Approach to the State Feedback Control of Nonlinear Dynamical Systems
• A new adaptive law for robust adaptation without persistent excitation
• Uncertainty, performance, and model dependency in approximate adaptive nonlinear control
• Adaptive output feedback control of a class of multi-input multi-output systems using neural networks
• Contril of rigid‐link, flexible‐joint robots:a survey of backstepping approaches
• Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

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