Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions

In this paper, adaptive neural network (NN) control is investigated for a class of block triangular multi-input multi-output (MIMO) nonlinear discrete-time systems with each subsystem in pure-feedback form with unknown control directions. Each subsystem is transformed into a predictor form such that the noncausal problem can be avoided in the control design. By exploring the properties of block triangular form, implicit controls are developed for each subsystem such that the couplings of inputs and states among subsystems have been completely decoupled. The high-order-neural-network (HONN) is employed to approximate the unknown control. Each subsystem achieves semi-global-uniformly-ultimately-bounded (SGUUB) stability with the proposed control and simulation results are presented to demonstrate its efficiency.