Adaptive neural control with fast approximation for uncertain nonlinear systems: a novel composite learning approach

DOI10.1002/asjc.3116MaRDI QIDQ6583459

Hao Dai, Jin-ping Jia, Hua Zhang, Jianbin Xie, Shaocong Wang

Publication date: 6 August 2024

Published in: Asian Journal of Control (Search for Journal in Brave)

zbMATH Keywords

neural networkexponential convergenceuncertain nonlinear systemadaptive neural controlcomposite learning

Mathematics Subject Classification ID

Systems theory; control (93-XX)

Cites Work

• Dynamic learning from adaptive neural control with predefined performance for a class of nonlinear systems
• Composite adaptive control of robot manipulators
• Composite adaptive control for Euler-Lagrange systems with additive disturbances
• Composite learning from adaptive backstepping neural network control
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• Persistency of Excitation in Identification Using Radial Basis Function Approximants
• Command Filtered Backstepping
• Combined/Composite Model Reference Adaptive Control
• A new approach to model reference adaptive control
• Adaptive Neural Dynamic Surface Control For Nonlinear Pure‐Feedback Systems With Multiple Time‐Varying Delays: A <scp>L</scp>yapunov‐<scp>R</scp>azumikhin Method
• Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation
• Adaptive backstepping tracking control of a car with \(n\) trailers based on RBF neural network
• Sliding mode control for underactuated system with input constraint based on RBF neural network and Hurwitz stability analysis
• Neural network-based continuous finite-time tracking control for uncertain robotic systems with actuator saturation

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