Dynamic output feedback stabilization for nonlinear systems based on standard neural network models.
A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.
Liu
Meiqin
M
Department of System Science and Engineering, School of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China. liumeiqin@cee.zju.edu.cn
D000465:Algorithms; D005246:Feedback / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software / Q000639:trends; D013997:Time Factors