Neural Net-Based H∞ Control for a Class of Nonlinear Systems
Neural Processing Letters
International Journal of Systems Science
Neural Network-Based H ∞Filtering for Nonlinear Jump Systems
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
Dual network based adaptive controller for pitch control of an aircraft
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
Design of a fuzzy takagi-sugeno controller to vary the joint knee angle of paraplegic patients
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Reliable robust controller design for nonlinear state-delayed systems based on neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Stochastic optimal control of nonlinear jump systems using neural networks
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Nonlinear system stabilisation by an evolutionary neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Nonlinear discrete system stabilisation by an evolutionary neural network
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
| Hi-index | 0.00 |
This paper discusses stability of neural network (NN)-based control systems using Lyapunov approach. First, it is pointed out that the dynamics of NN systems can be represented by a class of nonlinear systems treated as linear differential inclusions (LDI). Next, stability conditions for the class of nonlinear systems are derived and applied to the stability analysis of single NN systems and feedback NN control systems. Furthermore, a method of parameter region (PR) representation, which graphically shows the location of parameters of nonlinear systems, is proposed by introducing new concepts of vertex point and minimum representation. From these concepts, an important theorem, which is useful for effectively finding a Lyapunov function, is derived. Stability criteria of single NN systems are illustrated in terms of PR representation. Finally, stability of feedback NN control systems, which consist of a plant represented by an NN and an NN controller, is analyzed