Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems
Tao CHENG
Frank L.LEWIS
摘要：In this paper,neural networks are used to approximately solve the finite-horizon constrained input H-infiniy state feedback control problem.The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game.The game value function is approximated by a neural network wlth timevarying weights.It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain.The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line.The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.
机标关键词：nonlinear systemsvalue functionneural networkstate feedback controlnonlinear control
分类号：TP3(计算技术、计算机技术)
资助基金：国家自然科学基金(ECS-0501451)Army Research Office(W91NF-05-1-0314)
论文发表日期：2007-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 1-11 )
英文信息