Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach
作者： Chenguang Yang , Shuzhi Sam Ge , Cheng Xiang , Tianyou Chai , Tong Heng Lee
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摘要: In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: (1) pure-feedback and (2) autoregressive moving average exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in design, both transformed predictor design. Implicit function theorem is used difficulty nonaffine appearance input. The problem lacking priori knowledge on directions solved by using discrete Nussbaum gain. high-order (HONN) employed approximate control. closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability tracking error made within neighborhood around zero. Simulation results presented demonstrate effectiveness proposed
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