Composite learning control of robotic systems: a least squares modulated approach

DOI10.1016/j.automatica.2019.108612zbMath1430.93144OpenAlexW2978213765WikidataQ127172972 ScholiaQ127172972MaRDI QIDQ2288614

Haoyong Yu, Yongping Pan, Kai Guo, Dongdong Zheng

Publication date: 20 January 2020

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2019.108612

zbMATH Keywords

adaptive controlleast squaresrobot manipulatorsMoore-Penrose pseudoinversecomposite learning

Mathematics Subject Classification ID

Automated systems (robots, etc.) in control theory (93C85) Least squares and related methods for stochastic control systems (93E24) Asymptotic stability in control theory (93D20) Stochastic learning and adaptive control (93E35) Exponential stability (93D23)

Related Items (9)

Boundary adaptive fault-tolerant control for a flexible Timoshenko arm with backlash-like hysteresis ⋮ Composite learning adaptive dynamic surface control for uncertain nonlinear strict‐feedback systems with fixed‐time parameter estimation under sufficient excitation ⋮ Composite fuzzy voltage-based command-filtered learning control of electrically-driven robots with input delay using disturbance observer ⋮ Intelligent control for robotic manipulator with adaptive learning rate and variable prescribed performance boundaries ⋮ Event-based adaptive neural network asymptotic tracking control for a class of nonlinear systems ⋮ Learning from adaptive control under relaxed excitation conditions ⋮ Fixed-time composite neural learning control of state-constrained nonlinear uncertain systems ⋮ Predefined-time parameter estimation via modified dynamic regressor extension and mixing ⋮ Adaptive robust simultaneous stabilization of multiple \(n\)-degree-of-freedom robot systems

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