Jan 19, 2024 · This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to compensate for the ...

Jan 19, 2024 · This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to compensate ...

This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to compensate for the ...

Lyapunov-based arguments are applied to ensure the exponential stability of the closed-loop system. Numerical studies are performed to depict the ...

A composite learning control strategy is proposed for a class of strict‐feedback nonlinear systems with mismatched uncertainties, where raised‐cosine radial ...

May 17, 2019 · In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, ...

May 27, 2019 · In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, ...

Parameter convergence is desirable in adaptive control as it enhances the overall stability and robustness properties of the closed-loop system.

This paper proposes a composite learning backstepping control (CLBC) strategy based on modular backstepping and high-order tuners to compensate for the ...

The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems.