title: Neural-network-based near-optimal control for a class of discrete-time affine nonlinear systems with control constraints.
abstract: In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme.
authors: School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China. hgzhang@ieee.org
authors: Huaguang
authors: H
authors: Zhang
authors: Yanhong
authors: Y
authors: Luo
authors: Derong
authors: D
authors: Liu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors