Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach

Authors:
Chenguang Yang;Shuzhi Sam Ge;Cheng Xiang;Tianyou Chai;Tong Heng Lee
Affiliations:
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore;-;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2008

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Abstract

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) nonlinear pure-feedback systems and (2) nonlinear autoregressive moving average with exogenous inputs (NARMAX) systems. To overcome the noncausal problem, which has been known to be a major obstacle in the discrete-time control design, both systems are transformed to a predictor for output feedback control design. Implicit function theorem is used to overcome the difficulty of the nonaffine appearance of the control input. The problem of lacking a priori knowledge on the control directions is solved by using discrete Nussbaum gain. The high-order neural network (HONN) is employed to approximate the unknown control. The closed-loop system achieves semiglobal uniformly-ultimately-bounded (SGUUB) stability and the output tracking error is made within a neighborhood around zero. Simulation results are presented to demonstrate the effectiveness of the proposed control.