Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach.
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.
Yang
Chenguang
C
Social Robotics Lab, Interactive Digital Media Institute (IDMI), National University of Singapore, Singapore117576, Singapore. cgyang82@gmail.com
Ge
Shuzhi Sam
SS
Xiang
Cheng
C
Chai
Tianyou
T
Lee
Tong Heng
TH
D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted