Gradient Descent-Barzilai Borwein-Based Neural Network Tracking Control for Nonlinear Systems With Unknown Dynamics
- 8 July 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks and Learning Systems
- Vol. 34 (1) , 305-315
Abstract
In this article, a combined gradient descent-Barzilai Borwein (GD-BB) algorithm and radial basis function neural network (RBFNN) output tracking control strategy was proposed for a family of nonlinear systems with unknown drift function and control input gain function. In such a method, a neural network (NN) is used to approximate the controller directly. The main merits of the proposed strategy are given as follows: first, not only the NN parameters, such as weights, centers, and widths but also the learning rates of NN parameter updating laws are updated online via the proposed learning algorithm based on Barzilai-Borwein technique; and second, the controller design process can be further simplified, the controller parameters that should be tuned can be greatly reduced. Theoretical analysis about the stability of the closed-loop system is manifested. In addition, simulations were conducted on a numerical discrete time system and an inverted pendulum system to validate the presented control strategy.Keywords
Funding Information
- National Natural Science Foundation of China (61803122, 61773143)
- Higher Education Discipline Innovation Project (B16014)
- Special Project of Heilongjiang Postdoctoral Science Foundation (LBH-TZ2011)
This publication has 24 references indexed in Scilit:
- A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process ControlIEEE Transactions on Neural Networks and Learning Systems, 2015
- Backstepping Fuzzy-Neural-Network Control Design for Hybrid Maglev Transportation SystemIEEE Transactions on Neural Networks and Learning Systems, 2014
- Nonlinear Systems Modeling Based on Self-Organizing Fuzzy-Neural-Network With Adaptive Computation AlgorithmIEEE Transactions on Cybernetics, 2013
- Development and application of a novel radial basis function sliding mode controllerMechatronics, 2003
- The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization ProblemSIAM Journal on Optimization, 1997
- Stable adaptive neural control scheme for nonlinear systemsIEEE Transactions on Automatic Control, 1996
- On the Barzilai and Borwein choice of steplength for the gradient methodIMA Journal of Numerical Analysis, 1993
- Universal Approximation Using Radial-Basis-Function NetworksNeural Computation, 1991
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- Two-Point Step Size Gradient MethodsIMA Journal of Numerical Analysis, 1988