Neural Network-based e-Adaptive Dynamic Programming and e-Optimal Control for Nonlinear Systems
thesisposted on 2012-12-09, 00:00 authored by Ning Jin
By applying a novel e-optimal control performance index function as an approximation of the optimal performance index function, the e-optimal control theory and e-adaptive dynamic programming algorithms are established. The e-optimal control theory provides a new sense to overcome the "curse of dimensionality" problem and the "over optimal" problem of the optimal control theory. An algorithm of e-adaptive dynamic programming for discrete time systems using neural networks is given for general nonlinear system as well as a fast iterated algorithm is designed for the case that the utility function is quadratic. Furthermore, a novel wavelet basis function neural network (WBFNN) is defined for sequential learning during the numerical simulations of e-adaptive dynamic programming, which is an improvement of the radial basis function neural network (RBFNNs) and the wavelet neural network (WNN).
DepartmentElectrical and Computer Engineering
Degree GrantorUniversity of Illinois at Chicago
Committee MemberDevroye, Natasha Mazumder, Sudip Schonfeld, Dan Darabi, Houshang