Adaptive learning control of complex uncertain systems with nonlinear parameterization

In this paper, an adaptive learning control law is proposed to address complex uncertain systems with nonlinear parameterization. Specifically, the controller consists of: (i) a feedback type term, (ii) an adaptive mechanism for the unknown system parameters, and (iii) a learning-based technique to estimate the unknown periodic functions. As proven by a Lyapunov-based stability analysis, the designed adaptive learning control achieves global asymptotic tracking result for the system state while compensates for the uncertainty associated with the system parameters and the unknown periodic functions simultaneously