Hybrid Intelligent Output-Feedback Control for Trajectory Tracking of Uncertain Nonlinear Multivariable Dynamical Systems

Abstract

Output-feedback control for trajectory tracking is an important research topic of various engineering systems. In this paper, a novel online hybrid direct/indirect adaptive Petri fuzzy neural network (PFNN) controller with stare observer for uncertain nonlinear multivariable dynamical systems using generalized projection-update laws is presented. This new approach consists of control objectives determination, approximator configuration design, system dynamics modeling, online control algorithm development, and system stability analysis. According to the importance and viability of plant knowledge and control knowledge, a weighting factor is utilized to sum together the direct and indirect adaptive PFNN controllers. Therefore, the controller design methodology is more flexible during the design process. Besides, an improved generalized projection-update law is utilized to tune the adjustable parameters to prevent parameter drift. To illustrate the effectiveness of the proposed online hybrid PFNN controller and observer-design methodology, numerical simulation results for inverted pendulum systems and rigid robot manipulators are given in this paper.