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- W2019478222 abstract "It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm." @default.
- W2019478222 created "2016-06-24" @default.
- W2019478222 creator A5000266144 @default.
- W2019478222 creator A5012004938 @default.
- W2019478222 date "2012-12-01" @default.
- W2019478222 modified "2023-10-06" @default.
- W2019478222 title "Neural Network Based Online Simultaneous Policy Update Algorithm for Solving the HJI Equation in Nonlinear <formula formulatype=inline><tex Notation=TeX>$H_{infty}$</tex></formula> Control" @default.
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- W2019478222 doi "https://doi.org/10.1109/tnnls.2012.2217349" @default.
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