Adaptive direct RBFNN consensus control for a class of unknown nonlinear underactuated systems

Abstract

The consensus tracking control of a class of nonlinear underactuated multi-agent systems with uncertainties is studied in this paper. A radial basis function neural network (RBFNN)-based direct adaptive control algorithm is designed. Unlike many previous articles in which a neural network is employed to identify the unknown nonlinear functions in the system model or controller, this method directly approximates the ideal control law by a neural network, making the control law simpler. Based on the neural network direct control algorithm, a controller with a single-parameter learning scheme is designed. This new control method reduces the computational burden by reducing the amount of computational data. Finally, the closed-loop system is proved to be ultimately uniformly bounded stable; the application examples of the controllers are given by simulation.