Adaptive Neural Network L2-Gain Control for Unmodeled Dynamics Nonlinear System

Ge Si Wang; Zhi Gang Yu; Shen Min Song

doi:10.4028/www.scientific.net/KEM.419-420.561

Paper Titles

Experimental Research on Diamond String Bead Wire Cutting the Steel-Based Material
p.545

Technical Parameters of Rice-Straw Board with Fire-Retardant of Wood
p.549

A Knowledge-Based System for Product Design with Modules
p.553

Utilizing Restricted Direction Strategy and Binary Heap Technology to Optimize Dijkstra Algorithm in WebGIS
p.557

Adaptive Neural Network L₂-Gain Control for Unmodeled Dynamics Nonlinear System
p.561

A Stereo Vision Based Target Tracking and Obstacle Avoidance
p.565

Image Processing Algorithm for Tracking and Picking an Object for an Open Platform Mobile Robot
p.569

Passivity-Based Rotor Time Constant Estimation and Adaptive Position Control of Induction Machines
p.573

On the Firing Test Properties of Thinfilm Metallic Initiators
p.577

HomeKey Engineering MaterialsKey Engineering Materials Vols. 419-420Adaptive Neural Network L2-Gain Control for...

Adaptive Neural Network L₂-Gain Control for Unmodeled Dynamics Nonlinear System

Abstract:

A new control scheme is proposed for the nonlinear system with unmodeled dynamics based on HJI and adaptive neural network L2-gain controller. The aim is to make the closed-loop system a finite gain L2 stable between the disturbance and output of the system. The approach will avoid the limit of precise modelling, only need the unmodeled dynamics to be bounded without the other transcendent information. The simulation results show that the control system designed can satisfy the performance indicators proposed.