Title :
Gain-scheduling for systems with repeated scalar nonlinearities
Author :
Chu, Yun-Chung ; Glover, Keith
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong
Abstract :
The class of nonlinear systems described by a discrete-time state-equation containing a repeated scalar nonlinearity as in recurrent neural networks is considered. Given a plant of this form, sufficient conditions are derived for the synthesis of a controller of the same form so that the induced norm of the closed-loop is under a prescribed level, using positive definite diagonally dominant storage functions. Several of these conditions can be written into linear matrix inequalities
Keywords :
control nonlinearities; control system synthesis; discrete time systems; matrix algebra; nonlinear control systems; recurrent neural nets; closed-loop; discrete-time state-equation; gain-scheduling; induced norm; linear matrix inequalities; nonlinear systems; positive definite diagonally dominant storage functions; recurrent neural networks; repeated scalar nonlinearities; sufficient conditions; Automation; Control system synthesis; Linear matrix inequalities; Network synthesis; Neural networks; Nonlinear systems; Recurrent neural networks; Sufficient conditions; Symmetric matrices; Upper bound;
Conference_Titel :
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-4187-2
DOI :
10.1109/CDC.1997.650657
