Output regulation of nonlinear uncertain system with non-minimum phase via enhanced RBFN controller

Abstract

Presents an enhanced direct adaptive scheme for a radial basis function network (RBFN) based controller capable of online stably adjusting both the RBFN's output weights and variance parameters in the hidden nodes. The improved RBFN system is employed to online reconstruct the ideal control law adaptively and force the plant output to asymptotically track the output of a reference model, which is predetermined to stabilize robustly the unstable internal dynamics of a nonlinear uncertain system with nonminimum phase. A case study is utilized for demonstrating the effectiveness of the proposed scheme. Simulation results indicate that the transient responses of balancing the inverted pendulum system are markedly improved by using the enhanced algorithm proposed herein.