Title :
Application of dynamic neural networks to approximation and control of nonlinear systems
Author :
Amin, S. Massoud ; Rodin, Ervin Y. ; Wu, Alan Y.
Author_Institution :
Dept. of Syst. Sci. & Math., Washington Univ., St. Louis, MO, USA
Abstract :
Based on a paradigm of neurons with local memory (NLM), we discuss the representation of control systems by neural networks. Using this formulation, the basic issues of controllability and observability for the dynamic system are addressed. A separation principle of learning and control is presented for NLM, showing that the weights of the network do not affect its dynamics. Theoretical issues concerning local linearization via a coordinate transformation and nonlinear feedback are discussed
Keywords :
controllability; dynamics; feedback; learning (artificial intelligence); neurocontrollers; nonlinear control systems; observability; controllability; coordinate transformation; dynamic neural networks; local linearization; local memory; nonlinear feedback; nonlinear systems; observability; separation principle; Artificial neural networks; Control systems; Controllability; Mathematics; Neural networks; Neurofeedback; Neurons; Nonlinear control systems; Nonlinear systems; Observability;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.611790
