Logic switching based online periodic adaptive learning control algorithm dealing with unknown period and bound of the uncertain parameter

Abstract

In this paper, a switching periodic adaptive control approach is proposed for a class of nonlinear systems with periodic parametric uncertainties whose period and bound are not known. A fully saturated periodic adaptation law is utilized to estimate the unknown parameter vector. A logic switching based algorithm is provided to tune the unknown period and bound of the parameter vector online. By virtue of Lyapunov energy function, asymptotic convergence can be ensured for the tracking error and all the signals in the system is guaranteed bounded. A simulation to a one-link robotic manipulator is carried out to demonstrate the effectiveness of the switching learning control algorithm.