Composite learning control with modular backstepping and high-order tuners
This work proposes a composite learning backstepping control strategy using modular backstepping and high-order tuners to achieve closed-loop exponential stability for uncertain nonlinear systems. It eliminates the need for high-gain feedback and persistent excitation, improving transient performance by maximizing parameter estimation strength under weaker excitation conditions.