Title :
Adaptive neural observer with forward co-state propagation
Author :
Salam, Fathi M. ; Zhang, Jian
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan State Univ., East Lansing, MI, USA
Abstract :
An adaptive nonlinear observer using input and output measurements is described by using techniques of optimization theory, the calculus of variations and gradient descent dynamics. A series of formulations of general parameterized nonlinear observers of continuous-time and discrete-time are given, including a co-state (sensitivity) dynamics equation that propagates forward in time and serves as a filtered version of the measured error signal. Several Matlab simulation examples in the continuous-time and discrete-time cases are given to illustrate the approach
Keywords :
adaptive systems; continuous time systems; discrete time systems; dynamics; feedforward neural nets; nonlinear systems; observers; adaptive nonlinear observer; continuous-time systems; costate propagation; discrete-time systems; dynamics; gradient descent dynamics; multilayer neural networks; nonlinear systems; optimization; sensitivity; state estimation; Calculus; Electric variables measurement; Mathematical model; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Observers; Performance analysis; State estimation;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939105
